diff --git a/modsim_toolbox/.gitignore b/modsim_toolbox/.gitignore
new file mode 100644
index 0000000..d30925e
--- /dev/null
+++ b/modsim_toolbox/.gitignore
@@ -0,0 +1,223 @@
+.DS_Store
+.dev/
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[codz]
+*$py.class
+
+# C extensions
+*.so
+
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+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#   Usually these files are written by a python script from a template
+#   before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
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+
+# Installer logs
+pip-log.txt
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+.coverage
+.coverage.*
+.cache
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+coverage.xml
+*.cover
+*.py.cover
+*.lcov
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
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+# Sphinx documentation
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+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
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+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
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+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+# Pipfile.lock
+
+# UV
+#   Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+# uv.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+# poetry.lock
+# poetry.toml
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#   pdm recommends including project-wide configuration in pdm.toml, but excluding .pdm-python.
+#   https://pdm-project.org/en/latest/usage/project/#working-with-version-control
+# pdm.lock
+# pdm.toml
+.pdm-python
+.pdm-build/
+
+# pixi
+#   Similar to Pipfile.lock, it is generally recommended to include pixi.lock in version control.
+# pixi.lock
+#   Pixi creates a virtual environment in the .pixi directory, just like venv module creates one
+#   in the .venv directory. It is recommended not to include this directory in version control.
+.pixi/*
+!.pixi/config.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule*
+celerybeat.pid
+
+# Redis
+*.rdb
+*.aof
+*.pid
+
+# RabbitMQ
+mnesia/
+rabbitmq/
+rabbitmq-data/
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+.env
+.envrc
+.venv
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+/site
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+.mypy_cache/
+.dmypy.json
+dmypy.json
+
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+.pyre/
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+# pytype static type analyzer
+.pytype/
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+# Cython debug symbols
+cython_debug/
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+# PyCharm
+#   JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#   be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#   and can be added to the global gitignore or merged into this file.  For a more nuclear
+#   option (not recommended) you can uncomment the following to ignore the entire idea folder.
+# .idea/
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+.abstra/
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+# Visual Studio Code
+#   Visual Studio Code specific template is maintained in a separate VisualStudioCode.gitignore 
+#   that can be found at https://github.com/github/gitignore/blob/main/Global/VisualStudioCode.gitignore
+#   and can be added to the global gitignore or merged into this file. However, if you prefer, 
+#   you could uncomment the following to ignore the entire vscode folder
+# .vscode/
+# Temporary file for partial code execution
+tempCodeRunnerFile.py
+
+# Ruff stuff:
+.ruff_cache/
+
+# PyPI configuration file
+.pypirc
+
+# Marimo
+marimo/_static/
+marimo/_lsp/
+__marimo__/
+
+# Streamlit
+.streamlit/secrets.toml
diff --git a/modsim_toolbox/.python-version b/modsim_toolbox/.python-version
new file mode 100644
index 0000000..e4fba21
--- /dev/null
+++ b/modsim_toolbox/.python-version
@@ -0,0 +1 @@
+3.12
diff --git a/modsim_toolbox/README.md b/modsim_toolbox/README.md
new file mode 100644
index 0000000..5c0e8b3
--- /dev/null
+++ b/modsim_toolbox/README.md
@@ -0,0 +1,45 @@
+# ModSim Toolbox
+
+ModSim Toolbox is a minimal example of building reusable, accelerated, and
+differentiable infrastructure for multimodal simulation on top of
+[fVDB](https://github.com/AcademySoftwareFoundation/openvdb/tree/master/fvdb).
+It is not a simulator for any particular sensor. Instead, it factors out the
+geometric work that most sensor simulations share, so that the physics
+specific to a given modality can be written separately, on top of a common
+tensor-based interface.
+
+## Components
+
+The toolbox covers three areas:
+
+- **geometry** — rotations and the coordinate frames a sensor is mounted in.
+- **voxels** — converting group-jagged meshes into an `fvdb.GridBatch`, and
+  carrying per-corner attributes onto the resulting voxels.
+- **ray tracing** — casting a bundle of rays against a voxel grid batch using
+  fVDB's voxel ray tracing, finding the nearest surface each ray hits, and
+  computing visibility between a surface and a direction (for example,
+  toward a light source).
+
+There is also a viewer (`visualization.Visualizer`) for displaying the
+result. Because ray tracing runs against voxel grids rather than triangle
+meshes, and because fVDB's operations are differentiable, the same trace can
+be used both for fast forward simulation and for gradient-based tasks such as
+calibration or inverse rendering.
+
+The toolbox does not model physics: it has no notion of spectra, materials,
+or units of radiance, and it does not read files. Every function operates on
+tensors and returns tensors, and the modules are stateless, with the
+exception of `visualization.Visualizer`, which owns a live viser server. What
+a modality's outputs represent — radiance, a range value, a label — is
+decided by the caller. This is what allows the same underlying trace to
+support different modalities, such as a panchromatic camera or a
+spectrometer, without changes to the toolbox itself.
+
+## Example
+
+[`examples/car`](examples/car) shows one modality built on the toolbox: a
+DIRSIG vehicle-glint scene, voxelized per OBJ group, imaged by the
+panchromatic frame camera described in the scene's own platform file, and
+shaded using solar irradiance and Beard-Maxwell BRDFs. The physics specific
+to that example — spectral reflectance, shading, band integration — is
+implemented in the example itself, not in the toolbox.
diff --git a/modsim_toolbox/examples/car/.gitignore b/modsim_toolbox/examples/car/.gitignore
new file mode 100644
index 0000000..307b457
--- /dev/null
+++ b/modsim_toolbox/examples/car/.gitignore
@@ -0,0 +1 @@
+/scene
diff --git a/modsim_toolbox/examples/car/README.md b/modsim_toolbox/examples/car/README.md
new file mode 100644
index 0000000..72fa8af
--- /dev/null
+++ b/modsim_toolbox/examples/car/README.md
@@ -0,0 +1,43 @@
+# Car: vehicle-glint example
+
+This example uses the toolbox to simulate a panchromatic frame camera imaging
+a car, based on RIT's DIRSIG vehicle-glint demo scene. The toolbox handles
+voxelization and ray tracing; this example adds the optical physics —
+solar irradiance, Beard-Maxwell BRDFs, spectral reflectance, and band
+integration — in `radiometry.py` and `shade`.
+
+## Layout
+
+| File | Contents |
+|---|---|
+| `main.py` | `build_scene`, `build_sensor`, `simulate`, `serve` |
+| `radiometry.py` | Solar spectrum, Beard-Maxwell, Fresnel, spectral resampling |
+| `loader.py` | Wavefront OBJ and DIRSIG `.platform` / `.ppd` / `.mat` / `.ems` / `.fit` parsing |
+
+## Scene data
+
+The scene data is not checked in. Download the DIRSIG vehicle-glint demo
+scene from the [DIRSIG demo scenes page](https://dirsig.cis.rit.edu/) and
+unpack it into `scene/`, subject to RIT's license terms for that data. This
+example reads five files from it:
+
+```
+scene/geometry/infiniti_g35_vn.obj   car geometry, per group, with vertex normals
+scene/demo.platform                  320x240 at 16 um, f = 100 mm, 0.400-0.800 um "Pan"
+scene/demo.ppd                       platform pose: 150 m up, yawed 135 deg
+scene/materials/demo.mat             material table, referencing:
+scene/materials/*.ems, *.fit         emissivity curves and Beard-Maxwell fits
+```
+
+The 6 km ground plane declared in the scene is replaced with a 40 m patch,
+and capture time and location are constants in `main.py` rather than parsed
+from the scene files. Everything else the demo ships is unused.
+
+## Running it
+
+```bash
+uv run python main.py
+```
+
+This opens a viser page showing the voxelized scene alongside the simulated
+focal plane, with controls for solar azimuth, elevation, and shadows.
diff --git a/modsim_toolbox/examples/car/loader.py b/modsim_toolbox/examples/car/loader.py
new file mode 100644
index 0000000..fdf7947
--- /dev/null
+++ b/modsim_toolbox/examples/car/loader.py
@@ -0,0 +1,556 @@
+"""Load Wavefront OBJ files and DIRSIG scene files into GPU tensors.
+
+:class:`Material`, :class:`Channel` and :class:`Instrument` are the data
+objects those files are loaded into.
+"""
+
+from __future__ import annotations
+
+import re
+from dataclasses import dataclass
+from pathlib import Path
+from xml.etree import ElementTree
+
+import numpy as np
+import torch
+
+
+@dataclass(frozen=True)
+class Material:
+    """One ``MATERIAL_ENTRY`` of a DIRSIG ``.mat`` file.
+
+    Lambertian ``ClassicEmissivity`` materials have all Beard-Maxwell fields
+    set to ``None``; ``ShellTarget`` materials carry the full set. The fit
+    nearest the sensor's band is used, since the two visible-band entries in
+    these files are identical.
+
+    Attributes:
+        id: The ``usemtl`` id the geometry refers to this material by.
+        name: Human readable material name.
+        reflectance: Spectral reflectance as ``(wavelength, value)`` tensors,
+            derived from the emissivity curve as ``1 - emissivity``.
+            Wavelengths in microns, shape: (S,) each. S: spectral samples.
+        n: Real part of the complex index of refraction, ``None`` for
+            Lambertian materials.
+        k: Imaginary part (extinction coefficient) of the index.
+        dhr: Directional hemispherical reflectance of the first-surface term,
+            which sets its absolute scale.
+        bias: Amplitude of the Gaussian microfacet orientation distribution.
+        sigma: Width of that Gaussian, in radians of half-vector tilt.
+        tau: Beard-Maxwell shadowing decay constant, in degrees of bistatic
+            angle.
+        omega: Beard-Maxwell obscuration constant, in degrees of half-vector
+            tilt.
+        rho_d: Lambertian volumetric reflectance.
+        rho_v: Directional-diffuse volumetric reflectance.
+    """
+
+    id: int
+    name: str
+    reflectance: tuple[torch.Tensor, torch.Tensor]
+    n: float | None = None
+    k: float | None = None
+    dhr: float | None = None
+    bias: float | None = None
+    sigma: float | None = None
+    tau: float | None = None
+    omega: float | None = None
+    rho_d: float | None = None
+    rho_v: float | None = None
+
+    @property
+    def is_specular(self) -> bool:
+        """Whether this material has a first-surface (glinting) lobe."""
+        return self.n is not None
+
+    @property
+    def brdf(self) -> dict[str, float]:
+        """The Beard-Maxwell parameters, ready to splat into the BRDF calls.
+
+        Returns:
+            The eight lobe and volumetric parameters keyed by the argument names
+            :func:`radiometry.beard_maxwell` and
+            :func:`radiometry.dhr_specular_scale` use. Empty for a Lambertian
+            material.
+        """
+        keys = ("n", "k", "bias", "sigma", "tau", "omega", "rho_d", "rho_v")
+        return {key: value for key in keys if (value := getattr(self, key)) is not None}
+
+
+@dataclass(frozen=True)
+class Channel:
+    """One spectral channel of the focal plane's response.
+
+    Attributes:
+        name: Channel name as it appears in the platform file, e.g. ``"Pan"``.
+        center: Center wavelength, in microns.
+        width: Full width of the channel's passband, in microns.
+    """
+
+    name: str
+    center: float
+    width: float
+
+
+@dataclass(frozen=True)
+class Instrument:
+    """A generic DIRSIG instrument: its optics, focal plane and mounting.
+
+    Attributes:
+        focal_length: Distance from the entrance pupil to the focal plane, in
+            millimeters. DIRSIG declares no unit for it; millimeters is the
+            format's convention.
+        x_count: Number of detector elements along the focal plane's x axis.
+        y_count: Number of elements along its y axis.
+        x_spacing: Center-to-center element pitch along x, in millimeters.
+        y_spacing: Pitch along y, in millimeters.
+        x_flip: Whether the x element index runs opposite to focal plane x.
+        y_flip: Whether the y element index runs opposite to focal plane y.
+        mount_rotation: Euler xyz rotation of the mount relative to the
+            platform, in radians, shape: (3,).
+        bandpass: Wavelength ``(minimum, maximum)`` the focal plane responds
+            over, in microns.
+        channels: The focal plane's spectral channels, in file order.
+    """
+
+    focal_length: float
+    x_count: int
+    y_count: int
+    x_spacing: float
+    y_spacing: float
+    x_flip: bool
+    y_flip: bool
+    mount_rotation: np.ndarray
+    bandpass: tuple[float, float]
+    channels: tuple[Channel, ...]
+
+
+def load_emissivity(path: Path) -> tuple[torch.Tensor, torch.Tensor]:
+    """Read the first curve of a DIRSIG ``.ems`` emissivity file.
+
+    The format is a count of curves, then 91 further header lines, then one or
+    more ``CURVE_BEGIN`` blocks of ``wavelength emissivity`` pairs. Files may
+    hold many curves -- ``grass_mixed.ems`` has 300 realizations of a grass
+    mixture -- and only the first is used here, which is the simplification
+    this renderer makes in place of DIRSIG's per-facet material sampling.
+
+    Args:
+        path: Path to the ``.ems`` file.
+
+    Returns:
+        A pair ``(wavelength, emissivity)`` with wavelengths in microns,
+        shape: (S,) each. S: number of samples in the curve.
+    """
+    lines = path.read_text().splitlines()
+    start = next(i for i, line in enumerate(lines) if line.strip() == "CURVE_BEGIN")
+
+    wavelength: list[float] = []
+    value: list[float] = []
+    for line in lines[start + 1 :]:
+        if line.strip() == "CURVE_BEGIN":
+            break
+        fields = line.split()
+        if len(fields) == 2:
+            wavelength.append(float(fields[0]))
+            value.append(float(fields[1]))
+
+    return (
+        torch.tensor(wavelength, dtype=torch.float64),
+        torch.tensor(value, dtype=torch.float64),
+    )
+
+
+def _payloads(text: str, keyword: str) -> list[str]:
+    """Collect the payload of every line opening with ``keyword``.
+
+    Args:
+        text: Block of OBJ text to scan.
+        keyword: Line keyword to match, such as ``v`` or ``f``.
+
+    Returns:
+        The remainder of each matching line, keyword and its space stripped.
+    """
+    prefix = keyword + " "
+    return [
+        line[len(prefix) :] for line in text.splitlines() if line.startswith(prefix)
+    ]
+
+
+def _float_rows(lines: list[str], device: str) -> torch.Tensor:
+    """Parse whitespace-separated ``xyz`` payloads into one tensor.
+
+    Trailing columns (vertex colors, the ``w`` of a rational position) are
+    dropped. Parsing is batched through numpy so a whole table is converted at
+    C speed and reaches the device in a single transfer.
+
+    Args:
+        lines: Payload of each ``v`` or ``vn`` line.
+        device: Torch device to place the result on.
+
+    Returns:
+        Parsed rows, shape: (N, 3). N: number of lines.
+    """
+    if not lines:
+        return torch.empty(0, 3, device=device)
+    return torch.from_numpy(
+        np.array([line.split()[:3] for line in lines], dtype=np.float32)
+    ).to(device)
+
+
+def load_obj(
+    path: Path, device: str = "cuda"
+) -> tuple[
+    list[torch.Tensor],
+    list[torch.Tensor],
+    list[torch.Tensor],
+    list[str],
+    list[int],
+]:
+    """Load a Wavefront OBJ, jagged over its ``g`` groups.
+
+    A group is the unit of authoring in a modelling package and the unit of
+    simulation here, so it is the jagged dimension: groups hold wildly
+    different vertex counts. A ``g`` line opens a group and everything up to
+    the next one belongs to it, so the file is split on those and read one
+    block at a time, each block yielding the group's own tensors.
+
+    OBJ indices are global and cumulative, and a file may declare its vertices
+    all at once up front or block by block, so the ``v`` and ``vn`` tables are
+    accumulated as the blocks are read and a group is resolved against
+    everything declared so far. A group is assumed to reference only its own
+    vertices, so its slice of the table is the span between the lowest and
+    highest position index its faces touch, and rebasing the faces onto that
+    slice is all the reconciliation needed.
+
+    Normals are not group-local: a block may reference a ``vn`` declared in an
+    earlier one, so they stay indices into the accumulated table. They are also
+    stored per face corner rather than per vertex, because the OBJ picks a
+    ``vn`` per corner independently of the position — a hard crease reuses a
+    position with a different normal on each side, which the corner represents
+    directly instead of splitting the vertex to encode it. A corner with no
+    ``vn`` takes the flat geometric normal of its own triangle.
+
+    Args:
+        path: Path to the ``.obj`` file.
+        device: Torch device to place all tensors on.
+
+    Returns:
+        A five-tuple describing one group per ``g`` block that declares faces,
+        in file order. The first three are lists of length G, one tensor per
+        group, ready to hand to :class:`fvdb.JaggedTensor` once any further
+        groups have been appended:
+
+        - ``vertices``: Group-local world-space vertex positions, shape:
+          (V_g, 3) each. G: number of groups; V_g: vertices in group g.
+        - ``normals``: Per-corner normals, three independent vectors per face,
+          shape: (F_g, 3, 3) each. F_g: triangles in group g. Not unit length;
+          the OBJ's ``vn`` values are passed through as written.
+        - ``faces``: Triangle corner indices into the group's own vertices,
+          shape: (F_g, 3) each.
+        - ``group_names``: Name of each group, length G.
+        - ``material_ids``: ``usemtl`` id of each group, length G (``-1`` if
+          absent).
+    """
+    # Anything before the first ``g`` is header and any tables the file
+    # declares up front; each split that follows is one group's block, opening
+    # with its name.
+    preamble, *blocks = re.split(r"^g[ \t]+", path.read_text(), flags=re.MULTILINE)
+
+    group_names: list[str] = []
+    material_ids: list[int] = []
+    group_vertices: list[torch.Tensor] = []
+    group_normals: list[torch.Tensor] = []
+    group_faces: list[torch.Tensor] = []
+
+    # Positions and normals contributed by the preamble and every block read so
+    # far, which is all the current group may reference.
+    v_blocks = [_float_rows(_payloads(preamble, "v"), device)]
+    vn_blocks = [_float_rows(_payloads(preamble, "vn"), device)]
+
+    for block in blocks:
+        name, _, body = block.partition("\n")
+        v_blocks.append(_float_rows(_payloads(body, "v"), device))
+        vn_blocks.append(_float_rows(_payloads(body, "vn"), device))
+
+        v = torch.cat(v_blocks)  # (V_so_far, 3)
+        # Zero row appended so the flat-normal branch below always has a row to
+        # read, including for a file that declares no ``vn`` at all.
+        vn = torch.cat(vn_blocks + [torch.zeros(1, 3, device=device)])
+
+        # Corner indices into the file's tables, three corners per triangle.
+        # Polygons are fan-triangulated about their first corner, and a corner
+        # written without a ``vn`` gets index -1.
+        positions: list[int] = []
+        normal_refs: list[int] = []
+        for face in _payloads(body, "f"):
+            corner_v: list[int] = []
+            corner_vn: list[int] = []
+            for token in face.split():
+                parts = token.split("/")
+                corner_v.append(int(parts[0]) - 1)
+                corner_vn.append(
+                    int(parts[2]) - 1 if len(parts) > 2 and parts[2] else -1
+                )
+            for i in range(1, len(corner_v) - 1):
+                for c in (0, i, i + 1):
+                    positions.append(corner_v[c])
+                    normal_refs.append(corner_vn[c])
+
+        if not positions:
+            continue
+
+        v_index = torch.tensor(positions, dtype=torch.int64, device=device).reshape(
+            -1, 3
+        )  # (F_g, 3)
+        vn_index = torch.tensor(normal_refs, dtype=torch.int64, device=device).reshape(
+            -1, 3
+        )  # (F_g, 3)
+
+        # The group's own span of the table, with its faces rebased onto it.
+        base = int(v_index.min())
+        vertices = v[base : int(v_index.max()) + 1]  # (V_g, 3)
+        faces = (v_index - base).int()  # (F_g, 3)
+
+        # Flat geometric normal of each triangle, the fallback for corners the
+        # file leaves without a ``vn``.
+        corners = vertices[faces.long()]  # (F_g, 3, 3)
+        face_normal = torch.nn.functional.normalize(
+            torch.linalg.cross(
+                corners[:, 1] - corners[:, 0],
+                corners[:, 2] - corners[:, 0],
+                dim=-1,
+            ),
+            dim=-1,
+        )  # (F_g, 3)
+
+        materials = _payloads(body, "usemtl")
+        group_names.append(name.strip())
+        material_ids.append(int(materials[-1]) if materials else -1)
+        group_vertices.append(vertices)
+        group_normals.append(
+            torch.where(
+                (vn_index >= 0)[..., None],
+                vn[vn_index.clamp(min=0)],
+                face_normal[:, None, :],
+            )
+        )  # (F_g, 3, 3)
+        group_faces.append(faces)
+
+    return group_vertices, group_normals, group_faces, group_names, material_ids
+
+
+def ground_plane_mesh(
+    half_extent: float = 20.0,
+    z: float = 0.0,
+    device: str = "cuda",
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    """Build a flat rectangular mesh at height ``z`` for voxelization.
+
+    The returned tensors are a single geometry group shaped like one entry of
+    what :func:`load_obj` returns, so they can be appended to its lists before
+    constructing a :class:`fvdb.JaggedTensor`.
+
+    Args:
+        half_extent: Half-width of the square patch along each axis, in scene
+            meters.
+        z: Height of the plane in scene meters.
+        device: Torch device to place all tensors on.
+
+    Returns:
+        A 3-tuple of:
+
+        - ``vertices``: Corner positions, shape: (4, 3).
+        - ``normals``: Per-corner normals aligned with ``faces``, all pointing
+          up (+z), shape: (2, 3, 3). F: triangles (2).
+        - ``faces``: Triangle corner indices into ``vertices``, shape: (2, 3).
+    """
+    e = half_extent
+    vertices = torch.tensor(
+        [[-e, -e, z], [e, -e, z], [e, e, z], [-e, e, z]],
+        dtype=torch.float32,
+        device=device,
+    )
+    faces = torch.tensor([[0, 1, 2], [0, 2, 3]], dtype=torch.int32, device=device)
+    up = torch.zeros(2, 3, 3, device=device)
+    up[..., 2] = 1.0
+    return vertices, up, faces
+
+
+def _text(element: ElementTree.Element, tag: str) -> str:
+    """Return the text of a required child element, asserting it is present."""
+    value = element.findtext(tag)
+    assert value is not None, f"Required element {tag!r} missing"
+    return value
+
+
+# DIRSIG declares spatial and angular units as attributes on the element that
+# carries the values, so the scale factor is looked up rather than assumed.
+_LENGTH_TO_MM = {"microns": 1e-3, "millimeters": 1.0, "meters": 1e3}
+_LENGTH_TO_METERS = {"millimeters": 1e-3, "meters": 1.0, "kilometers": 1e3}
+_ANGLE_TO_RADIANS = {"radians": 1.0, "degrees": float(np.pi / 180.0)}
+
+
+def load_platform(path: Path) -> Instrument:
+    """Read a DIRSIG ``.platform`` file's instrument description.
+
+    Only the first enabled focal plane of the first instrument is read, which is
+    all this demo's platform declares.
+
+    Args:
+        path: Path to the ``.platform`` file.
+
+    Returns:
+        The instrument, with every length converted to millimeters and every
+        angle to radians.
+    """
+    root = ElementTree.parse(path).getroot()
+
+    mount = root.find(".//mount/data")
+    assert mount is not None
+    mount_scale = _ANGLE_TO_RADIANS[mount.get("angularunits", "radians")]
+    mount_rotation = mount_scale * np.array(
+        [float(_text(mount, f"{axis}rotation")) for axis in "xyz"], dtype=np.float64
+    )
+
+    array = root.find(".//detectorarray")
+    assert array is not None
+    to_mm = _LENGTH_TO_MM[array.get("spatialunits", "microns")]
+
+    bandpass = root.find(".//spectralresponse/bandpass")
+    assert bandpass is not None
+    return Instrument(
+        focal_length=float(_text(root, ".//instrument/properties/focallength")),
+        x_count=int(_text(array, "xelementcount")),
+        y_count=int(_text(array, "yelementcount")),
+        x_spacing=to_mm * float(_text(array, "xelementspacing")),
+        y_spacing=to_mm * float(_text(array, "yelementspacing")),
+        x_flip=bool(int(_text(array, "xflipaxis"))),
+        y_flip=bool(int(_text(array, "yflipaxis"))),
+        mount_rotation=mount_rotation,
+        bandpass=(
+            float(_text(bandpass, "minimum")),
+            float(_text(bandpass, "maximum")),
+        ),
+        channels=tuple(
+            Channel(
+                name=channel.get("name", ""),
+                center=float(_text(channel, "center")),
+                width=float(_text(channel, "width")),
+            )
+            for channel in root.findall(".//channellist/channel")
+        ),
+    )
+
+
+def load_platform_motion(path: Path) -> tuple[np.ndarray, np.ndarray]:
+    """Read the platform's pose from a DIRSIG ``.ppd`` file.
+
+    Only the first entry is read: this demo's platform is static, so its single
+    entry holds for every capture time.
+
+    Args:
+        path: Path to the ``.ppd`` file.
+
+    Returns:
+        A pair of ``(position, orientation)`` in scene ENU coordinates.
+        ``position``: Platform location in meters, shape: (3,).
+        ``orientation``: Euler xyz rotation in radians, shape: (3,).
+    """
+    data = ElementTree.parse(path).getroot().find("data")
+    assert data is not None
+    # The camera model below rotates instrument-frame directions straight into
+    # scene ENU, which only holds for these two conventions.
+    assert data.get("rotationframe") == "sceneenu"
+    assert data.get("rotationorder") == "xyz"
+
+    entry = data.find("entry")
+    assert entry is not None
+    point = entry.find("position/location/point")
+    assert point is not None
+    triple = entry.find("orientation/eulerangles/cartesiantriple")
+    assert triple is not None
+    position = _LENGTH_TO_METERS[data.get("spatialunits", "meters")] * np.array(
+        [float(_text(point, axis)) for axis in "xyz"], dtype=np.float64
+    )
+    orientation = _ANGLE_TO_RADIANS[data.get("angularunits", "radians")] * np.array(
+        [float(_text(triple, axis)) for axis in "xyz"], dtype=np.float64
+    )
+    return position, orientation
+
+
+def load_materials(path: Path, wavelength: float) -> dict[int, Material]:
+    """Read a DIRSIG ``.mat`` file and everything its entries reference.
+
+    For ``ShellTarget`` entries the Beard-Maxwell parameters are read from the
+    ``.fit`` file referenced by ``BRDF_FIT_FILE`` and inlined directly onto the
+    :class:`Material`. The fit block whose ``LAMBDA`` is nearest
+    ``wavelength`` is used; the two visible-band blocks in these files carry
+    identical parameters so the spectral variation is left entirely to the
+    reflectance curve.
+
+    Args:
+        path: Path to the ``.mat`` file. Emissivity and fit files are resolved
+            relative to its directory, as ``demo.scene`` declares.
+        wavelength: Band center in microns, used to select the nearest BRDF fit
+            block.
+
+    Returns:
+        Every material in the file, keyed by its ``usemtl`` id.
+    """
+
+    def _number(block: str, key: str) -> float:
+        m = re.search(rf"\b{key}\s*=\s*(\S+)", block)
+        assert m is not None
+        return float(m.group(1))
+
+    result: dict[int, Material] = {}
+    for block in re.findall(
+        r"MATERIAL_ENTRY\s*\{(.*?)\n\}", path.read_text(), re.DOTALL
+    ):
+        emissivity_match = re.search(r"FILENAME\s*=\s*(\S+)", block) or re.search(
+            r"EMISSIVITY_FILE\s*=\s*(\S+)", block
+        )
+        fit_match = re.search(r"BRDF_FIT_FILE\s*=\s*(\S+)", block)
+
+        assert emissivity_match is not None
+        emissivity_wl, emissivity_val = load_emissivity(
+            path.parent / emissivity_match.group(1)
+        )
+
+        bm: dict[str, float] = {}
+        if fit_match:
+            fit_blocks: list[str] = re.findall(
+                r"FIT_PARAMS\s*\{(.*?)\n\}",
+                (path.parent / fit_match.group(1)).read_text(),
+                re.DOTALL,
+            )
+            parsed = [
+                {
+                    "n": _number(b, "N"),
+                    "k": _number(b, "K"),
+                    "dhr": _number(b, "DHR"),
+                    "bias": _number(b, "BIAS"),
+                    "sigma": _number(b, "SIGMA"),
+                    "tau": _number(b, "TAU"),
+                    "omega": _number(b, "OMEGA"),
+                    "rho_d": _number(b, "RHO_D"),
+                    "rho_v": _number(b, "RHO_V"),
+                    "_lambda": _number(b, "LAMBDA"),
+                }
+                for b in fit_blocks
+            ]
+            best = min(parsed, key=lambda f: abs(f["_lambda"] - wavelength))
+            bm = {k: v for k, v in best.items() if k != "_lambda"}
+
+        id_match = re.search(r"\bID\s*=\s*(\d+)", block)
+        name_match = re.search(r"\bNAME\s*=\s*(.+)", block)
+        assert id_match is not None and name_match is not None
+        material = Material(
+            id=int(id_match.group(1)),
+            name=name_match.group(1).strip(),
+            # Kirchhoff: an opaque surface reflects what it does not absorb.
+            reflectance=(emissivity_wl, 1.0 - emissivity_val),
+            **bm,
+        )
+        result[material.id] = material
+    return result
diff --git a/modsim_toolbox/examples/car/main.py b/modsim_toolbox/examples/car/main.py
new file mode 100644
index 0000000..101cb39
--- /dev/null
+++ b/modsim_toolbox/examples/car/main.py
@@ -0,0 +1,565 @@
+"""Simulate the DIRSIG vehicle glint demo with fVDB, and serve it in viser.
+
+The demo scene ships everything a sensor simulation needs, and this example
+reads all of it rather than hard-coding any of it:
+
+``geometry/infiniti_g35_vn.obj``
+    One Infiniti G35 with vertex normals, voxelized per OBJ group via
+    :func:`modsim_toolbox.voxelize.voxelize_mesh`.
+``demo.platform`` / ``demo.ppd``
+    A 320 x 240, 16 um focal plane behind a 100 mm lens, 150 m up and yawed
+    135 degrees. Parsed by :mod:`loader`, which casts one ray per element.
+``materials/demo.mat``
+    Spectral emissivities and Beard-Maxwell BRDF fits, loaded by
+    :mod:`loader` and evaluated by :mod:`radiometry`. The glossy paint and
+    glass are what glint.
+``demo.scene`` / ``demo.tasks``
+    Where and when: Rochester NY, 2010-08-03 11:06 local, which is what the
+    sun sliders default to.
+
+The four stages below run in the order they are written:
+:meth:`Scene.from_obj` turns the geometry into voxel grids,
+:meth:`Sensor.from_dirsig` reads the optics and the materials, :func:`simulate`
+casts one ray per detector element and shades what it hits, and :func:`serve`
+puts both in a browser. Every knob either stage has -- voxel sizing, the ground
+patch, the spectral grid, the capture time and place -- is a defaulted argument
+on the one that uses it, so the only module constants are the paths into the
+scene and the capture the scene was authored around.
+
+The radiometry is deliberately single-bounce: direct solar plus uniform diffuse
+sky, no interreflection and no path radiance between the scene and the
+platform. That is enough to reproduce what this demo exists to show -- glints
+off the car's specular paint and glass moving as the sun moves -- but it is not
+a substitute for DIRSIG's own radiometry solver.
+
+Wavelengths are microns and radiances are ``W / (m^2 sr)`` integrated over the
+channel's passband, unless stated otherwise.
+"""
+
+from __future__ import annotations
+
+import datetime as dt
+import time
+from pathlib import Path
+from typing import NamedTuple
+
+import fvdb
+import numpy as np
+import pandas as pd  # pyright: ignore[reportMissingImports]
+import pvlib.solarposition  # pyright: ignore[reportMissingImports]
+import torch
+
+from modsim_toolbox import geometry, trace, visualization, voxelize
+
+import loader  # pyright: ignore[reportImplicitRelativeImport]
+import radiometry  # pyright: ignore[reportImplicitRelativeImport]
+
+DEVICE = "cuda"
+
+SCENE_DIR = Path(__file__).parent / "scene"
+OBJ_FILE = SCENE_DIR / "geometry" / "infiniti_g35_vn.obj"
+PLATFORM_FILE = SCENE_DIR / "demo.platform"
+MOTION_FILE = SCENE_DIR / "demo.ppd"
+MATERIAL_FILE = SCENE_DIR / "materials" / "demo.mat"
+
+# Rochester NY, 2010-08-03 11:06 local, which is what demo.scene and demo.tasks
+# declare and what the sun sliders default to.
+CAPTURE_LATITUDE = 43.12
+CAPTURE_LONGITUDE = -77.67
+CAPTURE_TIME = dt.datetime(
+    2010, 8, 3, 10, 0, 0, tzinfo=dt.timezone(dt.timedelta(hours=-5))
+) + dt.timedelta(seconds=4000)
+
+
+class Scene(NamedTuple):
+    """The voxelized scene a ray is traced against.
+
+    Attributes:
+        topology: Voxel grids, one per OBJ group plus the ground.
+        normals: Unit surface normal per voxel, jagged shape: (G, M_g, 3).
+            G: number of groups; M_g: voxels in group g.
+        materials: ``usemtl`` id of the group each voxel belongs to, jagged
+            shape: (G, M_g).
+        names: Name of each group, length G.
+        voxel_sizes: Voxel edge length per group, in scene meters, shape: (G,).
+        ground_z: Height of the ground plane in scene meters.
+    """
+
+    topology: fvdb.GridBatch
+    normals: fvdb.JaggedTensor
+    materials: fvdb.JaggedTensor
+    names: list[str]
+    voxel_sizes: torch.Tensor
+    ground_z: float
+
+    @classmethod
+    def from_obj(
+        cls,
+        path: Path,
+        detail_fraction: float = 0.35,
+        voxel_size_bounds: tuple[float, float] = (0.001, 0.1),
+        ground_half_extent: float = 20.0,
+        ground_voxel_size: float = 0.5,
+        ground_z: float = 0.0,
+        ground_material_id: int = 8000,
+        device: str = DEVICE,
+    ) -> Scene:
+        """Voxelize an OBJ and the ground it sits on.
+
+        Each OBJ group is voxelized at its own tessellation, so the wing mirrors
+        resolve without the underbody costing a fortune, and the ground patch is
+        added as one more group at a size that only has to catch shadows.
+
+        The defaults describe this demo: the scene's own 6 km ground box, whose
+        top is at z = 0 and which ``geometry.glist`` gives material 8000, stood
+        in for by a patch just big enough to hold the car's shadow.
+
+        Args:
+            path: Path to the ``.obj`` file.
+            detail_fraction: Voxel size per group as a fraction of that group's
+                own tessellation. Smaller oversamples; larger blurs.
+            voxel_size_bounds: Floor and ceiling on that size, in scene meters.
+            ground_half_extent: Half-width of the square ground patch, in scene
+                meters.
+            ground_voxel_size: Voxel edge length of the ground, in scene meters.
+                It only has to catch shadows, so it is coarse.
+            ground_z: Height of the ground plane in scene meters.
+            ground_material_id: ``usemtl`` id to give the ground.
+            device: Torch device to build everything on.
+
+        Returns:
+            The voxelized scene.
+        """
+        vertices, normals, faces, names, material_ids = loader.load_obj(path, device)
+        sizes = voxelize.estimate_voxel_size(
+            fvdb.JaggedTensor(vertices),
+            fvdb.JaggedTensor(faces),
+            detail_scale=detail_fraction,
+            smallest=voxel_size_bounds[0],
+            largest=voxel_size_bounds[1],
+        )
+
+        ground_vertices, ground_normals, ground_faces = loader.ground_plane_mesh(
+            ground_half_extent, ground_z, device
+        )
+        names = names + ["ground"]
+        material_ids = material_ids + [ground_material_id]
+        voxel_sizes = torch.cat([sizes, sizes.new_tensor([ground_voxel_size])])
+
+        topology, raw_normals = voxelize.voxelize_mesh(
+            fvdb.JaggedTensor(vertices + [ground_vertices]),
+            fvdb.JaggedTensor(faces + [ground_faces]),
+            fvdb.JaggedTensor(normals + [ground_normals]),
+            voxel_sizes,
+        )
+        return cls(
+            topology=topology,
+            normals=raw_normals.jagged_like(
+                torch.nn.functional.normalize(raw_normals.jdata, dim=-1)
+            ),
+            # The trace reports which voxel it hit, so materials are resolved
+            # per voxel: every voxel of a group carries that group's ``usemtl``.
+            materials=voxelize.broadcast_to_voxels(
+                topology, torch.tensor(material_ids, device=topology.device)
+            ),
+            names=names,
+            voxel_sizes=voxel_sizes,
+            ground_z=ground_z,
+        )
+
+
+class Sensor(NamedTuple):
+    """The instrument, where it is, and what it is looking at things made of.
+
+    Attributes:
+        instrument: Optics and focal plane, from the platform file.
+        position: Platform location in scene ENU coordinates, in meters,
+            shape: (3,).
+        rotation: Rotation taking instrument-frame directions into scene ENU,
+            shape: (3, 3).
+        materials: Every material the scene declares, matched to a voxel by
+            :attr:`loader.Material.id`.
+        specular_scales: First-surface normalization for each entry of
+            ``materials``.
+        wavelength: Wavelengths the channel response is sampled at, in microns,
+            shape: (W,). W: number of spectral samples.
+        weights: Band-integration weight per wavelength, shape: (W,).
+    """
+
+    instrument: loader.Instrument
+    position: np.ndarray
+    rotation: torch.Tensor
+    materials: list[loader.Material]
+    specular_scales: list[float]
+    wavelength: torch.Tensor
+    weights: torch.Tensor
+
+    @classmethod
+    def from_dirsig(
+        cls,
+        platform_path: Path,
+        motion_path: Path,
+        material_path: Path,
+        spectral_step: float = 0.01,
+        device: str = DEVICE,
+    ) -> Sensor:
+        """Read the optics, the pose and the materials from DIRSIG scene files.
+
+        ``demo.platform`` declares a 0.400-0.800 um bandpass and a single
+        rectangular "Pan" channel spanning all of it, so the channel response is
+        sampled as a boxcar over a grid of that step.
+
+        Args:
+            platform_path: Path to the ``.platform`` file, giving the optics and
+                the focal plane.
+            motion_path: Path to the ``.ppd`` file, giving the platform's pose.
+            material_path: Path to the ``.mat`` file, and through it the
+                emissivity curves and BRDF fits it references.
+            spectral_step: Spacing of the wavelength grid the channel response
+                and every spectrum are sampled on, in microns.
+            device: Torch device to build everything on.
+
+        Returns:
+            The sensor.
+        """
+        instrument = loader.load_platform(platform_path)
+        position, orientation = loader.load_platform_motion(motion_path)
+
+        # Instrument frame -> platform frame -> scene ENU, composed as matrices.
+        rotation = geometry.euler_to_mat(
+            torch.as_tensor(orientation, dtype=torch.float32, device=device)
+        ) @ geometry.euler_to_mat(
+            torch.as_tensor(
+                instrument.mount_rotation, dtype=torch.float32, device=device
+            )
+        )
+
+        # Sampled and windowed in float64: at the ends of the bandpass the
+        # boxcar edge falls exactly on a sample, so which side of it that sample
+        # lands on is decided by the last bit of the comparison.
+        low, high = instrument.bandpass
+        grid = np.arange(low, high + 1e-9, spectral_step)
+        window = np.zeros_like(grid)
+        for channel in instrument.channels:
+            window[np.abs(grid - channel.center) <= 0.5 * channel.width] = 1.0
+        wavelength = torch.as_tensor(grid, dtype=torch.float32, device=device)
+        response = torch.as_tensor(window, dtype=torch.float32, device=device)
+
+        materials = list(
+            loader.load_materials(
+                material_path, float(np.mean(instrument.bandpass))
+            ).values()
+        )
+        return cls(
+            instrument=instrument,
+            position=position,
+            rotation=rotation,
+            materials=materials,
+            specular_scales=[specular_scale(m) for m in materials],
+            wavelength=wavelength,
+            weights=radiometry.band_weights(wavelength, response),
+        )
+
+
+def specular_scale(material: loader.Material) -> float:
+    """Normalization for a material's first-surface lobe.
+
+    Args:
+        material: The material to scale, specular or not.
+
+    Returns:
+        The multiplier that reproduces the material's declared DHR, or 1.0 for a
+        Lambertian material, which has no first-surface term to scale.
+    """
+    if not material.is_specular:
+        return 1.0
+    assert material.dhr is not None
+    return radiometry.dhr_specular_scale(material.dhr, **material.brdf)
+
+
+def shade(
+    hit: trace.Hit,
+    material: torch.Tensor,
+    sunlit: torch.Tensor,
+    view: torch.Tensor,
+    toward_sun: torch.Tensor,
+    elevation: float,
+    sensor: Sensor,
+    shadows: bool = True,
+) -> torch.Tensor:
+    """Compute band-integrated radiance toward the sensor for every ray.
+
+    Direct sunlight is scattered by the material's BRDF, the shadow mask gates
+    it, and a uniform sky adds a Lambertian term weighted by how much of the
+    hemisphere the surface can see.
+
+    Args:
+        hit: Where each ray met the scene. N: number of rays.
+        material: ``usemtl`` id of the surface at each hit, -1 where the ray
+            escaped, shape: (N,).
+        sunlit: True where the sun reaches the surface, shape: (N,), dtype bool.
+        view: Unit direction from each surface point back toward the sensor,
+            shape: (N, 3).
+        toward_sun: Unit direction from the scene toward the sun, shape: (3,).
+        elevation: Solar elevation in degrees above the horizon.
+        sensor: Supplies the material table and the channel weighting.
+        shadows: Whether the shadow mask gates the direct beam.
+
+    Returns:
+        Radiance in ``W / (m^2 sr)`` integrated over the channel, shape: (N,).
+    """
+    direct, diffuse = radiometry.solar_spectrum(sensor.wavelength, elevation)
+
+    cos_incident = (hit.normal * toward_sun).sum(-1)
+    cos_reflected = (hit.normal * view).sum(-1)
+    half = torch.nn.functional.normalize(toward_sun + view, dim=-1)
+    cos_half = (hit.normal * half).sum(-1)
+    cos_bistatic = (half * toward_sun).sum(-1)
+    lit = (cos_incident > 0.0) & (cos_reflected > 0.0) & hit.hit
+    if shadows:
+        lit &= sunlit
+
+    # How much of the sky hemisphere the surface sees, for the ambient term.
+    sky_view = (0.5 * (1.0 + hit.normal[:, 2])).clamp(0.0, 1.0)
+
+    radiance = torch.zeros(len(hit.point), device=hit.point.device)
+    for index, entry in enumerate(sensor.materials):
+        selected = material == entry.id
+        if not bool(selected.any()):
+            continue
+        wl, value = entry.reflectance
+        reflectance = radiometry.resample(
+            wl.to(DEVICE), value.to(DEVICE), sensor.wavelength
+        ).to(torch.float32)
+
+        if not entry.is_specular:
+            # Lambertian: SPECULAR_FRACTION is 0 for both ClassicEmissivity
+            # materials in this scene.
+            brdf = (reflectance / torch.pi)[None, :].expand(int(selected.sum()), -1)
+        else:
+            # The material's DHR is the first-surface share of the total
+            # reflectance; whatever is left scatters diffusely and carries the
+            # material's color.
+            assert entry.dhr is not None
+            lobe = radiometry.beard_maxwell(
+                cos_incident[selected],
+                cos_reflected[selected],
+                cos_half[selected],
+                cos_bistatic[selected],
+                specular_scale=sensor.specular_scales[index],
+                **entry.brdf,
+            )
+            body = ((reflectance - entry.dhr).clamp(min=0.0) / torch.pi)[None, :]
+            brdf = lobe[:, None] + body
+
+        beam = (
+            brdf
+            * direct[None, :]
+            * (cos_incident[selected] * lit[selected]).clamp(min=0.0)[:, None]
+        )
+        sky = (
+            (reflectance / torch.pi)[None, :]
+            * diffuse[None, :]
+            * sky_view[selected][:, None]
+        )
+        radiance[selected] = ((beam + sky) * sensor.weights[None, :]).sum(-1)
+
+    return torch.where(hit.hit, radiance, 0.0)
+
+
+def simulate(
+    scene: Scene,
+    sensor: Sensor,
+    azimuth: float,
+    elevation: float,
+    shadows: bool = True,
+) -> np.ndarray:
+    """Render one full-resolution frame of the focal plane.
+
+    Args:
+        scene: The voxelized scene to trace against.
+        sensor: The instrument to trace from.
+        azimuth: Solar azimuth in degrees, clockwise from north.
+        elevation: Solar elevation in degrees above the horizon.
+        shadows: Whether to trace shadow rays.
+
+    Returns:
+        Band-integrated radiance in ``W / (m^2 sr)``, shape: (H, W). H:
+        detector rows, y element count. W: columns, x element count.
+    """
+    instrument = sensor.instrument
+    origins, directions = trace.rays_from_pinhole(
+        instrument.focal_length,
+        instrument.x_count,
+        instrument.y_count,
+        instrument.x_spacing * (-1.0 if instrument.x_flip else 1.0),
+        instrument.y_spacing * (-1.0 if instrument.y_flip else 1.0),
+        torch.as_tensor(sensor.position, dtype=torch.float32, device=DEVICE),
+        sensor.rotation,
+    )
+
+    hit = trace.first_hit(scene.topology, scene.normals, origins, directions)
+    toward_sun = radiometry.sun_direction(azimuth, elevation).to(DEVICE)
+    sunlit = ~trace.shadowed(scene.topology, hit.point, hit.normal, toward_sun)
+
+    radiance = shade(
+        hit,
+        material=torch.where(hit.hit, scene.materials.jdata[hit.voxel], -1),
+        sunlit=sunlit,
+        view=-directions,
+        toward_sun=toward_sun,
+        elevation=elevation,
+        sensor=sensor,
+        shadows=shadows,
+    )
+    return radiance.reshape(instrument.y_count, instrument.x_count).cpu().numpy()
+
+
+def two_sigma_scale(image: np.ndarray, sigmas: float = 2.0) -> np.ndarray:
+    """Scale a high dynamic range image the way DIRSIG's viewer does.
+
+    The demo's own README points out that min/max scaling hides everything once
+    a glint saturates the range, and that its "two sigma" scaling is what makes
+    the scene legible. This is that: clip to the mean plus or minus a couple of
+    standard deviations, then stretch.
+
+    Args:
+        image: Radiance image, shape: (H, W). H: detector rows. W: columns.
+        sigmas: Half-width of the retained range, in standard deviations.
+
+    Returns:
+        Display image in ``[0, 255]``, shape: (H, W, 3), dtype uint8.
+    """
+    mean = float(image.mean())
+    deviation = float(image.std())
+    low = max(image.min(), mean - sigmas * deviation)
+    high = min(image.max(), mean + sigmas * deviation)
+    scaled = np.clip((image - low) / max(high - low, 1e-12), 0.0, 1.0)
+    return np.repeat((255.0 * scaled).astype(np.uint8)[:, :, None], 3, axis=2)
+
+
+def serve(
+    scene: Scene,
+    sensor: Sensor,
+    capture_time: dt.datetime = CAPTURE_TIME,
+    latitude: float = CAPTURE_LATITUDE,
+    longitude: float = CAPTURE_LONGITUDE,
+    port: int = 8080,
+) -> None:
+    """Show the voxels and the simulated focal plane in a viser page.
+
+    Blocks until the process is interrupted.
+
+    Args:
+        scene: The voxelized scene, drawn as colored point clouds.
+        sensor: The instrument to simulate with.
+        capture_time: When the scene is being imaged, which with the
+            geolocation sets where the sun sliders start.
+        latitude: Scene latitude in degrees north.
+        longitude: Scene longitude in degrees east.
+        port: TCP port for the viser web interface.
+    """
+    vis = visualization.Visualizer(host="0.0.0.0", port=port)
+    server = vis.server
+    vis.add_grid_batch(
+        "/voxels",
+        scene.topology,
+        scene.normals,
+        visualization.normal_colormap(),
+        point_size=scene.voxel_sizes * 0.5,
+        names=scene.names,
+    )
+
+    position = pvlib.solarposition.get_solarposition(
+        pd.DatetimeIndex([capture_time]), latitude, longitude
+    )
+    sun = (float(position["azimuth"].iloc[0]), float(position["elevation"].iloc[0]))
+    print(f"sun at capture time: azimuth {sun[0]:.1f} deg, elevation {sun[1]:.1f} deg")
+
+    with server.gui.add_folder("Sun"):
+        azimuth_slider = server.gui.add_slider(
+            "Azimuth (deg)", min=0.0, max=360.0, step=1.0, initial_value=sun[0]
+        )
+        elevation_slider = server.gui.add_slider(
+            "Elevation (deg)", min=0.0, max=90.0, step=1.0, initial_value=sun[1]
+        )
+        shadow_toggle = server.gui.add_checkbox("Shadows", initial_value=True)
+        reset_button = server.gui.add_button("Reset to capture time")
+        resimulate_button = server.gui.add_button("Resimulate")
+
+    status = server.gui.add_markdown("")
+    image_handle = server.gui.add_image(
+        np.zeros((sensor.instrument.y_count, sensor.instrument.x_count, 3), np.uint8),
+        label="Simulated focal plane",
+        format="png",
+    )
+
+    def resimulate() -> None:
+        """Re-render the focal plane for the sliders' current sun angle."""
+        azimuth, elevation = float(azimuth_slider.value), float(elevation_slider.value)
+        status.content = (
+            f"Simulating at azimuth {azimuth:.0f}, elevation {elevation:.0f}..."
+        )
+        start = time.perf_counter()
+        image = simulate(
+            scene, sensor, azimuth, elevation, shadows=bool(shadow_toggle.value)
+        )
+        image_handle.image = two_sigma_scale(image)
+        server.scene.add_point_cloud(
+            "/sun",
+            points=(200.0 * radiometry.sun_direction(azimuth, elevation))
+            .unsqueeze(0)
+            .numpy(),
+            colors=np.array([[255, 240, 120]], np.uint8),
+            point_size=6.0,
+            point_shape="circle",
+        )
+        status.content = (
+            f"azimuth {azimuth:.0f} deg, elevation {elevation:.0f} deg  \n"
+            f"radiance {image.min():.2f} to {image.max():.2f} W/(m^2 sr)  \n"
+            f"{time.perf_counter() - start:.2f} s"
+        )
+
+    @resimulate_button.on_click
+    def _(_event) -> None:
+        resimulate()
+
+    @reset_button.on_click
+    def _(_event) -> None:
+        azimuth_slider.value, elevation_slider.value = sun
+        resimulate()
+
+    resimulate()
+    print(f"viser running at http://localhost:{server.get_port()} (ctrl-c to exit)")
+    while True:
+        time.sleep(1.0)
+
+
+def main() -> None:
+    """Voxelize the car, read the sensor, and serve the simulation in viser."""
+    scene = Scene.from_obj(OBJ_FILE)
+    print(
+        f"{OBJ_FILE.stem}: {len(scene.names)} groups, "
+        f"{len(scene.normals.jdata)} voxels, "
+        f"{1000 * float(scene.voxel_sizes.min()):.1f}-"
+        f"{1000 * float(scene.voxel_sizes.max()):.1f} mm"
+    )
+
+    sensor = Sensor.from_dirsig(PLATFORM_FILE, MOTION_FILE, MATERIAL_FILE)
+    instrument = sensor.instrument
+    ifov = instrument.x_spacing / instrument.focal_length
+    altitude = sensor.position[2] - scene.ground_z
+    print(
+        f"sensor: {instrument.x_count}x{instrument.y_count} elements, "
+        f"{1000 * instrument.x_spacing:.1f} um pitch, f = {instrument.focal_length:.1f} mm, "
+        f"bandpass {instrument.bandpass[0]:.3f}-{instrument.bandpass[1]:.3f} um\n"
+        f"  IFOV {1e6 * ifov:.1f} urad, GSD {100 * ifov * altitude:.2f} cm at "
+        f"{altitude:.0f} m, materials "
+        + ", ".join(m.name[:24] for m in sensor.materials)
+    )
+
+    serve(scene, sensor)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/modsim_toolbox/examples/car/pyproject.toml b/modsim_toolbox/examples/car/pyproject.toml
new file mode 100644
index 0000000..6e1ae36
--- /dev/null
+++ b/modsim_toolbox/examples/car/pyproject.toml
@@ -0,0 +1,29 @@
+[project]
+name = "car-example"
+version = "0.1.0"
+description = "Vehicle glint scene example for the modsim toolbox"
+requires-python = ">=3.12"
+dependencies = [
+  "modsim-toolbox",
+  "numpy",
+  "pandas",
+  "pvlib>=0.11",
+]
+
+# `tool.uv.sources` is not inherited from a path dependency, so the index
+# routing that modsim-toolbox declares has to be repeated here. Keep the two
+# files in sync: fvdb-core wheels are built against one exact torch minor.
+[tool.uv.sources]
+modsim-toolbox = { path = "../..", editable = true }
+torch = { index = "pytorch-cu130" }
+fvdb-core = { index = "fvdb-release" }
+
+[[tool.uv.index]]
+name = "pytorch-cu130"
+url = "https://download.pytorch.org/whl/cu130"
+explicit = true
+
+[[tool.uv.index]]
+name = "fvdb-release"
+url = "https://d36m13axqqhiit.cloudfront.net/simple"
+explicit = true
diff --git a/modsim_toolbox/examples/car/radiometry.py b/modsim_toolbox/examples/car/radiometry.py
new file mode 100644
index 0000000..1de77a2
--- /dev/null
+++ b/modsim_toolbox/examples/car/radiometry.py
@@ -0,0 +1,329 @@
+"""The optical physics this example simulates.
+
+Sunlight reaching the ground, how a surface scatters it, and how a spectral
+curve is resampled and integrated against a channel. None of this belongs to
+the toolbox, which is deliberately modality-free; it is what makes *this*
+example a reflective-band optical simulation rather than some other kind.
+"""
+
+import math
+
+import torch
+
+# Solar constant, band-integrated irradiance at the top of the atmosphere, W/m^2.
+SOLAR_CONSTANT = 1361.0
+# Effective blackbody temperature of the sun's photosphere, in kelvin.
+SOLAR_TEMPERATURE = 5778.0
+# Angstrom turbidity parameters for the aerosol optical depth.
+AEROSOL_BETA = 0.05
+AEROSOL_ALPHA = 1.3
+
+
+def sun_direction(azimuth: float, elevation: float) -> torch.Tensor:
+    """Unit vector pointing at the sun, in scene ENU coordinates.
+
+    Args:
+        azimuth: Solar azimuth in degrees, clockwise from north.
+        elevation: Solar elevation in degrees above the horizon.
+
+    Returns:
+        Unit direction from the scene toward the sun, shape: (3,).
+    """
+    az = math.radians(azimuth)
+    el = math.radians(elevation)
+    return torch.tensor(
+        [
+            math.sin(az) * math.cos(el),
+            math.cos(az) * math.cos(el),
+            math.sin(el),
+        ],
+        dtype=torch.float32,
+    )
+
+
+def solar_spectrum(
+    wavelength: torch.Tensor, elevation: float
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """Direct and diffuse solar spectral irradiance at the ground.
+
+    The extraterrestrial spectrum is a 5778 K blackbody normalized to the solar
+    constant. It is attenuated by Rayleigh scattering and an Angstrom aerosol
+    term along the Kasten-Young air mass path; the diffuse sky term takes half
+    of what Rayleigh scattering removes, the standard first-order approximation
+    that also gives the sky its blue.
+
+    Args:
+        wavelength: Wavelengths to evaluate, in microns, shape: (W,). W: number
+            of wavelengths.
+        elevation: Solar elevation in degrees above the horizon.
+
+    Returns:
+        A pair of spectral irradiances in ``W / (m^2 um)``: the direct beam
+        measured normal to itself, shape: (W,), and the diffuse sky irradiance
+        on a horizontal surface, shape: (W,).
+    """
+    if elevation <= 0.0:
+        return torch.zeros_like(wavelength), torch.zeros_like(wavelength)
+
+    micron = wavelength * 1e-6
+    radiance = 1.0 / (
+        micron**5 * (torch.exp(0.01438777 / (micron * SOLAR_TEMPERATURE)) - 1.0)
+    )
+
+    # Normalize the full Planck integral to the solar constant.
+    full = torch.linspace(0.15, 5.0, 4000, device=wavelength.device) * 1e-6
+    total = torch.trapezoid(
+        1.0 / (full**5 * (torch.exp(0.01438777 / (full * SOLAR_TEMPERATURE)) - 1.0)),
+        full * 1e6,
+    )
+    top_of_atmosphere = SOLAR_CONSTANT * radiance / total
+
+    # Kasten-Young (1989) relative air mass.
+    sine = math.sin(math.radians(elevation))
+    air_mass: float = 1.0 / (sine + 0.50572 * (elevation + 6.07995) ** (-1.6364))
+
+    rayleigh = torch.exp(-air_mass * 0.008735 * wavelength**-4.08)
+    aerosol = torch.exp(-air_mass * AEROSOL_BETA * wavelength**-AEROSOL_ALPHA)
+
+    direct = top_of_atmosphere * rayleigh * aerosol
+    diffuse = 0.5 * top_of_atmosphere * sine * (1.0 - rayleigh) * aerosol
+    return direct, diffuse
+
+
+def beard_maxwell(
+    cos_incident: torch.Tensor,
+    cos_reflected: torch.Tensor,
+    cos_half: torch.Tensor,
+    cos_bistatic: torch.Tensor,
+    n: float,
+    k: float,
+    bias: float,
+    sigma: float,
+    tau: float,
+    omega: float,
+    rho_d: float,
+    rho_v: float,
+    specular_scale: float = 1.0,
+) -> torch.Tensor:
+    """Evaluate the NEF Beard-Maxwell BRDF.
+
+    Implements the Westlund-Meyer (Graphics Interface 2002) equation 10:
+
+        rho = R(beta)/R(0) * rho_fs(theta_h) * cos^2(theta_h)
+              / (cos(theta_i) cos(theta_r)) * SO
+            + rho_d + 2 rho_v / (cos(theta_i) + cos(theta_r))
+
+    where ``rho_fs`` is a Gaussian in half-vector tilt and SO is a
+    shadowing/obscuration factor.
+
+    Args:
+        cos_incident: Cosine between the surface normal and the direction to
+            the source, shape: (N,). N: number of samples.
+        cos_reflected: Cosine between the surface normal and the direction to
+            the sensor, shape: (N,).
+        cos_half: Cosine between the surface normal and the half vector
+            (microfacet tilt), shape: (N,).
+        cos_bistatic: Cosine between the incident direction and the half
+            vector (angle of incidence onto the microfacet), shape: (N,).
+        n: Real part of the complex index of refraction.
+        k: Imaginary part (extinction coefficient) of the index.
+        bias: Amplitude of the Gaussian microfacet orientation distribution.
+        sigma: Width of that Gaussian, in radians of half-vector tilt.
+        tau: Shadowing decay constant, in degrees of bistatic angle.
+        omega: Obscuration constant, in degrees of half-vector tilt.
+        rho_d: Lambertian volumetric reflectance.
+        rho_v: Directional-diffuse volumetric reflectance.
+        specular_scale: Multiplier on the first-surface term, used to match
+            the material's declared DHR.
+
+    Returns:
+        BRDF value in inverse steradians, shape: (N,).
+    """
+    theta_half = torch.arccos(cos_half.clamp(-1.0, 1.0))
+    beta = torch.arccos(cos_bistatic.clamp(-1.0, 1.0))
+
+    orientation = bias * torch.exp(-((theta_half / sigma) ** 2))
+
+    tilt = torch.rad2deg(theta_half) / omega
+    shadow = (1.0 + tilt * torch.exp(-2.0 * torch.rad2deg(beta) / tau)) / (1.0 + tilt)
+
+    fresnel_ratio = fresnel(cos_bistatic, n, k) / fresnel(
+        torch.ones_like(cos_bistatic), n, k
+    )
+    denominator = (cos_incident * cos_reflected).clamp(min=1e-4)
+    first_surface = (
+        specular_scale
+        * fresnel_ratio
+        * orientation
+        * cos_half.clamp(min=0.0) ** 2
+        / denominator
+        * shadow
+    )
+
+    volumetric = rho_d + 2.0 * rho_v / (cos_incident + cos_reflected).clamp(min=1e-4)
+    return first_surface + volumetric
+
+
+def fresnel(cos_angle: torch.Tensor, n: float, k: float) -> torch.Tensor:
+    """Unpolarized Fresnel reflectance of a surface with complex index n - ik.
+
+    Averages the two polarization states, which is what an unpolarized source
+    and a sensor with ``<polarizer type="none"/>`` between them see.
+
+    Args:
+        cos_angle: Cosine of the angle of incidence onto the microfacet,
+            shape: (N,). N: number of samples.
+        n: Real part of the index of refraction.
+        k: Extinction coefficient.
+
+    Returns:
+        Reflectance in ``[0, 1]``, shape: (N,).
+    """
+    # Floored away from zero: the parallel branch below divides by cos^2, which
+    # at exactly grazing incidence would produce inf / inf.
+    cos_sq = cos_angle.clamp(0.0, 1.0) ** 2 + 1e-12
+    sin_sq = (1.0 - cos_sq).clamp(min=0.0)
+
+    # Solve for the real and imaginary parts of the transmitted cosine.
+    common = n * n - k * k - sin_sq
+    radical = torch.sqrt((common**2 + (2.0 * n * k) ** 2).clamp(min=0.0))
+    a_sq = (0.5 * (radical + common)).clamp(min=0.0)
+    b_sq = (0.5 * (radical - common)).clamp(min=0.0)
+    a = torch.sqrt(a_sq)
+    cos = torch.sqrt(cos_sq)
+
+    perpendicular = (a_sq + b_sq - 2.0 * a * cos + cos_sq) / (
+        a_sq + b_sq + 2.0 * a * cos + cos_sq
+    )
+    parallel = perpendicular * (
+        (a_sq + b_sq - 2.0 * a * cos * torch.sqrt(sin_sq) + sin_sq * sin_sq / cos_sq)
+        / (a_sq + b_sq + 2.0 * a * cos * torch.sqrt(sin_sq) + sin_sq * sin_sq / cos_sq)
+    )
+    return (0.5 * (perpendicular + parallel)).clamp(0.0, 1.0)
+
+
+# Zenith and azimuth samples used to integrate a BRDF over the hemisphere when
+# solving for the DHR normalization. The specular lobes here are a few degrees
+# wide, so the zenith grid has to be fine to resolve them.
+DEVICE = "cuda"
+DHR_ZENITH_SAMPLES = 2048
+DHR_AZIMUTH_SAMPLES = 256
+
+
+def dhr_specular_scale(
+    dhr: float,
+    n: float,
+    k: float,
+    bias: float,
+    sigma: float,
+    tau: float,
+    omega: float,
+    rho_d: float = 0.0,
+    rho_v: float = 0.0,
+) -> float:
+    """Solve for the first-surface scale that reproduces a material's DHR.
+
+    A fit file declares a directional hemispherical reflectance but not how the
+    orientation Gaussian is normalized, so the lobe's absolute height is
+    undetermined. Integrating the unscaled first-surface term over the reflected
+    hemisphere at normal incidence and dividing the declared DHR by the result
+    recovers it, which is what NEF equation 11 does spectrally.
+
+    The integral is taken at normal incidence, where the lobe sits around the
+    normal and the quadrature resolves it.
+
+    Args:
+        dhr: Declared directional hemispherical reflectance of the first-surface
+            term.
+        n: Real part of the complex index of refraction.
+        k: Imaginary part (extinction coefficient) of the index.
+        bias: Amplitude of the Gaussian microfacet orientation distribution.
+        sigma: Width of that Gaussian, in radians of half-vector tilt.
+        tau: Shadowing decay constant, in degrees of bistatic angle.
+        omega: Obscuration constant, in degrees of half-vector tilt.
+        rho_d: Ignored. The DHR quantifies the first-surface lobe alone, so the
+            volumetric terms are held at zero for the integral; accepted only so
+            a material's full parameter set can be splatted in.
+        rho_v: Ignored, as ``rho_d``.
+
+    Returns:
+        Multiplier for the first-surface term of :func:`beard_maxwell`.
+    """
+    del rho_d, rho_v
+
+    zenith = (
+        torch.arange(DHR_ZENITH_SAMPLES, device=DEVICE, dtype=torch.float64) + 0.5
+    ) * (0.5 * math.pi / DHR_ZENITH_SAMPLES)
+    azimuth = (
+        torch.arange(DHR_AZIMUTH_SAMPLES, device=DEVICE, dtype=torch.float64) + 0.5
+    ) * (2.0 * math.pi / DHR_AZIMUTH_SAMPLES)
+    grid_zenith, grid_azimuth = torch.meshgrid(zenith, azimuth, indexing="ij")
+
+    # Normal incidence: the source is straight up, so theta_i = 0.
+    incident = torch.tensor([0.0, 0.0, 1.0], device=DEVICE, dtype=torch.float64)
+    reflected = torch.stack(
+        [
+            torch.sin(grid_zenith) * torch.cos(grid_azimuth),
+            torch.sin(grid_zenith) * torch.sin(grid_azimuth),
+            torch.cos(grid_zenith),
+        ],
+        dim=-1,
+    ).reshape(-1, 3)
+    half = torch.nn.functional.normalize(reflected + incident, dim=-1)
+
+    unscaled = beard_maxwell(
+        cos_incident=torch.ones(len(reflected), device=DEVICE, dtype=torch.float64),
+        cos_reflected=reflected[:, 2],
+        cos_half=half[:, 2],
+        cos_bistatic=(half * incident).sum(-1),
+        n=n,
+        k=k,
+        bias=bias,
+        sigma=sigma,
+        tau=tau,
+        omega=omega,
+        rho_d=0.0,
+        rho_v=0.0,
+    )
+
+    # Integrate f * cos(theta_r) over the hemisphere.
+    solid_angle = (
+        torch.sin(grid_zenith).reshape(-1)
+        * (0.5 * math.pi / DHR_ZENITH_SAMPLES)
+        * (2.0 * math.pi / DHR_AZIMUTH_SAMPLES)
+    )
+    return dhr / float((unscaled * reflected[:, 2] * solid_angle).sum())
+
+
+def resample(x: torch.Tensor, y: torch.Tensor, at: torch.Tensor) -> torch.Tensor:
+    """Linearly resample a tabulated curve, holding its end values beyond it.
+
+    Args:
+        x: Sample positions of the curve, ascending, shape: (S,). S: number of
+            tabulated samples.
+        y: Curve value at each, shape: (S,).
+        at: Positions to evaluate at, shape: (W,). W: number of query points.
+
+    Returns:
+        Curve value at each query position, shape: (W,).
+    """
+    i = torch.searchsorted(x.contiguous(), at.contiguous()).clamp(1, len(x) - 1)
+    t = ((at - x[i - 1]) / (x[i] - x[i - 1])).clamp(0.0, 1.0)
+    return y[i - 1] + t * (y[i] - y[i - 1])
+
+
+def band_weights(wavelength: torch.Tensor, response: torch.Tensor) -> torch.Tensor:
+    """Trapezoidal quadrature weights for integrating a spectrum over a channel.
+
+    Args:
+        wavelength: Wavelengths of the spectral samples, ascending, in microns,
+            shape: (W,). W: number of spectral samples.
+        response: Channel response at each wavelength, shape: (W,).
+
+    Returns:
+        Weight per sample, so that ``(spectrum * weights).sum(-1)`` is the
+        band-integrated quantity, shape: (W,).
+    """
+    delta = torch.diff(wavelength)
+    width = torch.cat([delta[:1], (wavelength[2:] - wavelength[:-2]) / 2, delta[-1:]])
+    return response * width
diff --git a/modsim_toolbox/examples/car/uv.lock b/modsim_toolbox/examples/car/uv.lock
new file mode 100644
index 0000000..9d0e936
--- /dev/null
+++ b/modsim_toolbox/examples/car/uv.lock
@@ -0,0 +1,1150 @@
+version = 1
+revision = 3
+requires-python = ">=3.12"
+resolution-markers = [
+    "python_full_version >= '3.14' and sys_platform == 'win32'",
+    "python_full_version >= '3.14' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version < '3.14' and sys_platform == 'win32'",
+    "python_full_version < '3.14' and sys_platform == 'emscripten'",
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diff --git a/modsim_toolbox/pyproject.toml b/modsim_toolbox/pyproject.toml
new file mode 100644
index 0000000..ba89d02
--- /dev/null
+++ b/modsim_toolbox/pyproject.toml
@@ -0,0 +1,50 @@
+[project]
+name = "modsim-toolbox"
+version = "0.1.0"
+description = "Accelerated and differentiable sensor simulation"
+readme = "README.md"
+authors = [
+    { name = "Gabriel Casselman", email = "gcasselman@nvidia.com" }
+]
+requires-python = ">=3.12"
+dependencies = [
+  # fvdb-core wheels are built against one exact torch minor (encoded in the
+  # "+ptXYZ.cuNNN" local version), so torch must be pinned to match. Bumping
+  # fvdb-core means bumping torch in lockstep:
+  #   fvdb-core 0.4.x -> torch 2.10   fvdb-core 0.5.x -> torch 2.11
+  "torch==2.10.*",
+  "fvdb-core==0.4.2",
+  "numpy",
+  "viser>=1.0",
+]
+
+[tool.uv.sources]
+torch = { index = "pytorch-cu130"}
+fvdb-core = { index = "fvdb-release"}
+
+[[tool.uv.index]]
+name = "pytorch-cu130"
+url = "https://download.pytorch.org/whl/cu130"
+explicit = true
+
+[[tool.uv.index]]
+name = "fvdb-release"
+url = "https://d36m13axqqhiit.cloudfront.net/simple"
+explicit = true
+
+
+[build-system]
+requires = ["uv_build>=0.11.17,<0.12.0"]
+build-backend = "uv_build"
+
+[tool.ruff]
+line-length = 88
+
+[tool.ruff.lint.isort]
+# The car example imports its own modules by bare name, as a flat script does.
+known-local-folder = ["loader", "radiometry"]
+
+[tool.basedpyright]
+typeCheckingMode = "standard"
+venvPath = "."
+venv = ".venv"
diff --git a/modsim_toolbox/src/modsim_toolbox/__init__.py b/modsim_toolbox/src/modsim_toolbox/__init__.py
new file mode 100644
index 0000000..83237b0
--- /dev/null
+++ b/modsim_toolbox/src/modsim_toolbox/__init__.py
@@ -0,0 +1,22 @@
+"""Reusable components for simulating imaging systems on voxelized scenes.
+
+The toolbox generalizes over three things and nothing else: **geometry**,
+**voxels**, and **ray tracing**, with a viewer for looking at the result. It
+holds no physics -- no spectra, no materials, no units of radiance -- and reads
+no files: every function takes tensors and returns tensors, so what a modality
+means is decided entirely by its caller. See ``examples/car`` for one worked
+modality end to end.
+
+Import the modules, not their contents, so that every call site says which of
+the four concepts it is reaching for::
+
+    from modsim_toolbox import geometry, trace, visualization, voxelize
+
+    topology, normals = voxelize.voxelize_mesh(vertices, faces, normals, size)
+    origins, directions = trace.rays_from_pinhole(...)
+    hit = trace.first_hit(topology, normals, origins, directions)
+"""
+
+from modsim_toolbox import geometry, trace, visualization, voxelize
+
+__all__ = ["geometry", "trace", "visualization", "voxelize"]
diff --git a/modsim_toolbox/src/modsim_toolbox/geometry.py b/modsim_toolbox/src/modsim_toolbox/geometry.py
new file mode 100644
index 0000000..6f3e942
--- /dev/null
+++ b/modsim_toolbox/src/modsim_toolbox/geometry.py
@@ -0,0 +1,26 @@
+"""Pure geometric utilities: rotations."""
+
+from __future__ import annotations
+
+import torch
+
+
+def euler_to_mat(angles: torch.Tensor) -> torch.Tensor:
+    """Build a 3×3 rotation matrix from Euler xyz angles.
+
+    The x rotation is applied first, then y, then z:
+    ``R = Rz @ Ry @ Rx``.
+
+    Args:
+        angles: Rotations about x, y and z axes in radians, shape: (3,).
+
+    Returns:
+        Rotation matrix, shape: (3, 3).
+    """
+    cx, cy, cz = torch.cos(angles).unbind()
+    sx, sy, sz = torch.sin(angles).unbind()
+    z, o = torch.zeros_like(cx), torch.ones_like(cx)
+    rx = torch.stack([o, z, z, z, cx, -sx, z, sx, cx]).reshape(3, 3)
+    ry = torch.stack([cy, z, sy, z, o, z, -sy, z, cy]).reshape(3, 3)
+    rz = torch.stack([cz, -sz, z, sz, cz, z, z, z, o]).reshape(3, 3)
+    return rz @ ry @ rx
diff --git a/modsim_toolbox/src/modsim_toolbox/py.typed b/modsim_toolbox/src/modsim_toolbox/py.typed
new file mode 100644
index 0000000..e69de29
diff --git a/modsim_toolbox/src/modsim_toolbox/trace.py b/modsim_toolbox/src/modsim_toolbox/trace.py
new file mode 100644
index 0000000..ef46b18
--- /dev/null
+++ b/modsim_toolbox/src/modsim_toolbox/trace.py
@@ -0,0 +1,250 @@
+"""Purely geometric ray tracing against a voxelized scene.
+
+Nothing here knows about spectra, materials, or units of radiance. A trace
+answers two questions: what surface a ray met, and which point sources that
+surface can see. Turning those answers into radiance is the caller's job.
+
+The scene topology is an :class:`fvdb.GridBatch`, one grid per group of the
+source geometry, each free to carry its own voxel size. A ray is traced against
+every grid and the nearest hit wins, so the batch is a single scene rather than
+a batch of independent scenes.
+"""
+
+from __future__ import annotations
+
+from typing import NamedTuple
+
+import fvdb
+import torch
+
+# Secondary rays start this many voxels off the surface so they do not
+# immediately re-hit the voxel they were spawned from.
+SURFACE_OFFSET_VOXELS = 1.5
+
+
+class Hit(NamedTuple):
+    """Where a bundle of rays first met the scene.
+
+    Attributes:
+        distance: Distance along each ray to the surface, in world units,
+            infinite where the ray escaped, shape: (N,). N: number of rays.
+        point: World-space intersection point, equal to the ray origin where
+            the ray escaped, shape: (N, 3).
+        normal: Unit surface normal at the intersection, zero where the ray
+            escaped, shape: (N, 3).
+        voxel: Index of the surface voxel into the batch's concatenated voxel
+            ordering, the ordering every per-voxel attribute uses, so any of
+            them can be looked up with it. Zero where the ray escaped,
+            shape: (N,).
+        hit: True where the ray met the scene, shape: (N,), dtype bool.
+    """
+
+    distance: torch.Tensor
+    point: torch.Tensor
+    normal: torch.Tensor
+    voxel: torch.Tensor
+    hit: torch.Tensor
+
+
+def rays_from_pinhole(
+    focal_length: float,
+    x_count: int,
+    y_count: int,
+    x_spacing: float,
+    y_spacing: float,
+    origin: torch.Tensor,
+    rotation: torch.Tensor,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """Generate scene-space rays for every pixel of a pinhole camera.
+
+    The boresight points along the camera's ``-z`` axis. A pixel at column
+    ``i`` and row ``j`` maps to focal-plane position
+    ``((i - 0.5*(x_count-1))*x_spacing, (j - 0.5*(y_count-1))*y_spacing)``,
+    giving unnormalized direction ``(x, y, -focal_length)`` before the rotation
+    is applied. Rays come back row-major, so a per-ray result reshapes straight
+    to an image of shape ``(y_count, x_count)``.
+
+    Args:
+        focal_length: Distance from the entrance pupil to the focal plane, in
+            the same units as ``x_spacing`` and ``y_spacing``.
+        x_count: Number of pixels along the camera's x axis.
+        y_count: Number of pixels along its y axis.
+        x_spacing: Center-to-center pixel pitch along x, in focal-plane units.
+            Pass a negative value to flip the x axis.
+        y_spacing: Center-to-center pixel pitch along y, in focal-plane units.
+            Pass a negative value to flip the y axis.
+        origin: Camera position in world coordinates, shape: (3,).
+        rotation: Rotation matrix taking camera-frame directions into the world
+            frame, shape: (3, 3).
+
+    Returns:
+        A pair of:
+
+        - Ray origins in world coordinates, one per pixel, shape: (N, 3).
+          N: number of pixels, ``x_count * y_count``.
+        - Unit ray directions in world coordinates, shape: (N, 3).
+    """
+    device = origin.device
+    dtype = origin.dtype
+    y_index, x_index = torch.meshgrid(
+        torch.arange(y_count, device=device, dtype=dtype),
+        torch.arange(x_count, device=device, dtype=dtype),
+        indexing="ij",
+    )
+    x_plane = (x_index - 0.5 * (x_count - 1)) * x_spacing
+    y_plane = (y_index - 0.5 * (y_count - 1)) * y_spacing
+    directions = torch.nn.functional.normalize(
+        torch.stack(
+            [x_plane, y_plane, torch.full_like(x_plane, -focal_length)], dim=-1
+        ).reshape(-1, 3),
+        dim=-1,
+    )
+    directions = directions @ rotation.to(device=device, dtype=dtype).T
+    return origin.unsqueeze(0).expand(len(directions), -1), directions
+
+
+def _march(
+    topology: fvdb.GridBatch,
+    origins: torch.Tensor,
+    directions: torch.Tensor,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """March one ray bundle against every grid in the batch, in a single call.
+
+    :meth:`fvdb.GridBatch.voxels_along_rays` pairs the g-th ray set with the
+    g-th grid, so the bundle is broadcast across the batch by handing it the
+    same rays G times: one DDA launch covers the whole scene rather than one
+    launch per grid. Results come back jagged over G * N rays in grid-major
+    order, which reshapes straight to (G, N).
+
+    Args:
+        topology: Scene topology, one grid per geometry group. G: number of
+            grids.
+        origins: Ray origins in world units, shape: (N, 3). N: number of rays.
+        directions: Unit ray directions, shape: (N, 3).
+
+    Returns:
+        A 2-tuple of:
+
+        - Distance along each ray to where it entered each grid, infinite where
+          the ray missed that grid, shape: (G, N).
+        - Index of the entered voxel into the batch's concatenated voxel
+          ordering, zero where the ray missed, shape: (G, N).
+    """
+    N = len(origins)
+    G = topology.grid_count
+    device = origins.device
+
+    offsets = torch.arange(G + 1, device=device) * N
+    rays = fvdb.JaggedTensor.from_data_and_offsets(
+        origins.expand(G, N, 3).reshape(G * N, 3), offsets
+    )
+    steps = fvdb.JaggedTensor.from_data_and_offsets(
+        directions.expand(G, N, 3).reshape(G * N, 3), offsets
+    )
+    # cumulative=True numbers voxels across the whole batch, so the returned
+    # indices address a per-voxel JaggedTensor's jdata directly.
+    voxels, times = topology.voxels_along_rays(
+        rays, steps, max_voxels=1, return_ijk=False, cumulative=True
+    )
+
+    # Each ray contributes zero or one entry, in ray order, so the jagged
+    # results scatter straight back into dense per-ray arrays.
+    found = (voxels.joffsets[1:] - voxels.joffsets[:-1]) > 0
+    entry = torch.full((G * N,), float("inf"), device=device)
+    entry[found] = times.jdata[:, 0]
+    index = torch.zeros(G * N, dtype=torch.long, device=device)
+    index[found] = voxels.jdata.long()
+    return entry.reshape(G, N), index.reshape(G, N)
+
+
+def first_hit(
+    topology: fvdb.GridBatch,
+    normals: fvdb.JaggedTensor,
+    origins: torch.Tensor,
+    directions: torch.Tensor,
+) -> Hit:
+    """March a ray bundle through every grid and keep the nearest surface.
+
+    Args:
+        topology: Scene topology, one grid per geometry group.
+        normals: Unit surface normal per voxel, ordered to match each grid's own
+            voxel ordering, jagged shape: (G, M_g, 3). G: number of grids;
+            M_g: voxels in grid g.
+        origins: Ray origins in world units, shape: (N, 3). N: number of rays.
+        directions: Unit ray directions, shape: (N, 3).
+
+    Returns:
+        The nearest surface each ray met, as a :class:`Hit`.
+    """
+    entry, voxel = _march(topology, origins, directions)
+
+    nearest = entry.argmin(0)
+    rays = torch.arange(len(origins), device=origins.device)
+    distance = entry[nearest, rays]
+    index = voxel[nearest, rays]
+    hit = torch.isfinite(distance)
+    normal = torch.where(hit[:, None], normals.jdata[index], 0.0)
+    point = origins + torch.where(hit, distance, 0.0)[:, None] * directions
+    return Hit(distance=distance, point=point, normal=normal, voxel=index, hit=hit)
+
+
+def visible(
+    topology: fvdb.GridBatch,
+    origins: torch.Tensor,
+    directions: torch.Tensor,
+    limit: torch.Tensor,
+) -> torch.Tensor:
+    """Test whether each ray reaches its endpoint without meeting the scene.
+
+    Args:
+        topology: Scene topology, one grid per geometry group.
+        origins: Ray origins in world units, shape: (N, 3). N: number of rays.
+        directions: Unit ray directions, shape: (N, 3).
+        limit: Distance along each ray to its endpoint, in world units. Surfaces
+            beyond it do not block, shape: (N,).
+
+    Returns:
+        True where nothing stands between the origin and the endpoint,
+        shape: (N,), dtype bool.
+    """
+    entry, _ = _march(topology, origins, directions)
+    return ~(entry < limit[None, :]).any(0)
+
+
+def shadowed(
+    topology: fvdb.GridBatch,
+    points: torch.Tensor,
+    normals: torch.Tensor,
+    directions: torch.Tensor,
+    limit: torch.Tensor | None = None,
+    offset_voxels: float = SURFACE_OFFSET_VOXELS,
+) -> torch.Tensor:
+    """Test whether scene geometry stands between each surface point and a source.
+
+    A shadow ray is spawned from each point toward the source and lifted off the
+    surface along the normal, so that it does not immediately re-hit the voxel it
+    came from. That lift is the whole reason this exists rather than a bare
+    :func:`visible` call.
+
+    Args:
+        topology: Scene topology, one grid per geometry group.
+        points: World-space surface points, shape: (N, 3). N: number of points.
+        normals: Unit surface normal at each point, along which the shadow ray
+            is lifted, shape: (N, 3).
+        directions: Unit direction from each point toward the source, shape:
+            (N, 3). A single direction of shape (3,) broadcasts to every point,
+            which is what a source at infinity looks like.
+        limit: Distance from each point to the source, in world units, so that
+            geometry behind the source does not shadow it, shape: (N,). Pass
+            ``None`` for a source at infinity, where nothing is behind it.
+        offset_voxels: How far to lift the shadow ray off the surface, in
+            multiples of the largest voxel edge length in the batch.
+
+    Returns:
+        True where the source is occluded, shape: (N,), dtype bool.
+    """
+    offset = offset_voxels * float(topology.voxel_sizes.max())
+    directions = directions.expand_as(points)
+    if limit is None:
+        limit = torch.full((len(points),), float("inf"), device=points.device)
+    return ~visible(topology, points + offset * normals, directions, limit - offset)
diff --git a/modsim_toolbox/src/modsim_toolbox/visualization.py b/modsim_toolbox/src/modsim_toolbox/visualization.py
new file mode 100644
index 0000000..034149f
--- /dev/null
+++ b/modsim_toolbox/src/modsim_toolbox/visualization.py
@@ -0,0 +1,113 @@
+"""3D visualization utilities built on top of viser.
+
+Wraps a :class:`viser.ViserServer` with helpers for displaying fVDB
+:class:`~fvdb.GridBatch` objects, colored by arbitrary per-voxel tensors through
+a pluggable color mapping function.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Callable
+
+import fvdb
+import numpy as np
+import torch
+import viser  # pyright: ignore[reportMissingImports]
+
+from modsim_toolbox import voxelize
+
+# A color function maps a per-voxel tensor of shape (N, ...) to an (N, 3)
+# uint8 RGB array. The exact inner shape depends on the colormap.
+ColorFn = Callable[[torch.Tensor], np.ndarray]
+
+
+def normal_colormap() -> ColorFn:
+    """Return a color function that encodes unit normals as RGB.
+
+    Each axis is linearly mapped from ``[-1, 1]`` to ``[0, 255]``, so +x reads
+    red, +y green, and +z blue. Opposing faces of a thin panel appear as
+    complementary colors, which makes a mis-attributed normal easy to spot.
+
+    Returns:
+        A function that accepts normals of shape ``(N, 3)`` and returns
+        ``uint8`` colors of shape ``(N, 3)``.
+    """
+
+    def _fn(normals: torch.Tensor) -> np.ndarray:
+        return (127.5 * (normals + 1.0)).clamp(0, 255).to(torch.uint8).cpu().numpy()
+
+    return _fn
+
+
+class Visualizer:
+    """A viser-backed 3D visualizer with fVDB grid helpers.
+
+    Wraps :class:`viser.ViserServer` so that callers can display
+    :class:`~fvdb.GridBatch` objects without touching the viser API directly.
+    The server is started on construction and stays running until the process
+    exits.
+
+    Args:
+        host: Network interface to bind the HTTP server to.
+        port: TCP port for the viser web interface.
+    """
+
+    def __init__(self, host: str = "0.0.0.0", port: int = 8080) -> None:
+        self._server = viser.ViserServer(host=host, port=port)
+        self._server.scene.set_up_direction("+z")
+
+    @property
+    def server(self) -> viser.ViserServer:
+        """The underlying viser server, for callers that need direct access."""
+        return self._server
+
+    def add_grid_batch(
+        self,
+        path: str,
+        grids: fvdb.GridBatch,
+        values: fvdb.JaggedTensor,
+        color_fn: ColorFn,
+        point_size: float | torch.Tensor = 0.05,
+        point_shape: str = "rounded",
+        names: list[str] | None = None,
+    ) -> None:
+        """Display every grid in a :class:`~fvdb.GridBatch` as a colored point cloud.
+
+        One viser point cloud is created per grid, placed under ``path/<name>``
+        (or ``path/<g>`` when ``names`` is not supplied). Each voxel's world
+        position is computed from the grid's IJK coordinates; its color comes
+        from applying ``color_fn`` to the matching row of ``values``.
+
+        Args:
+            path: Base scene path. Each grid is added as a child, e.g.
+                ``/voxels/body``.
+            grids: Voxelized scene, one grid per group.
+            values: Per-voxel attribute fed to ``color_fn``, jagged shape:
+                ``(G, N_g, ...)``. G: number of grids; N_g: voxels in grid g.
+            color_fn: Maps a per-voxel tensor of shape ``(N_g, ...)`` to
+                ``uint8`` colors of shape ``(N_g, 3)``.
+            point_size: Rendered radius of each point in world units. Either a
+                single float applied to every grid, or a 1-D tensor of shape
+                ``(G,)`` with one radius per grid.
+            point_shape: Viser point shape, e.g. ``"rounded"`` or ``"circle"``.
+            names: Labels for each grid, used as the last component of the
+                scene path. Defaults to the grid index as a string.
+        """
+        sizes: torch.Tensor | float
+        if isinstance(point_size, torch.Tensor):
+            sizes = point_size.float().cpu()
+        else:
+            sizes = point_size
+
+        centers = voxelize.voxel_centers(grids)
+        for g in range(grids.grid_count):
+            label = names[g] if names is not None else str(g)
+            colors = color_fn(values[g].jdata)
+            size = float(sizes[g]) if isinstance(sizes, torch.Tensor) else sizes
+            self._server.scene.add_point_cloud(
+                f"{path}/{label}",
+                points=centers[g].jdata.cpu().numpy(),
+                colors=colors,
+                point_size=size,
+                point_shape=point_shape,
+            )
diff --git a/modsim_toolbox/src/modsim_toolbox/voxelize.py b/modsim_toolbox/src/modsim_toolbox/voxelize.py
new file mode 100644
index 0000000..bc4a9b9
--- /dev/null
+++ b/modsim_toolbox/src/modsim_toolbox/voxelize.py
@@ -0,0 +1,219 @@
+"""Turning meshes into voxel grids, and carrying attributes onto the voxels.
+
+Every function here is jagged over the source geometry's groups: a group is the
+unit of authoring, so it is the unit of voxelization, and each group becomes one
+grid of an :class:`fvdb.GridBatch` free to carry its own voxel size.
+"""
+
+import math
+
+import fvdb
+import torch
+
+
+def voxelize_mesh(
+    vertices: fvdb.JaggedTensor,
+    faces: fvdb.JaggedTensor,
+    values: fvdb.JaggedTensor,
+    voxel_size: float | torch.Tensor,
+) -> tuple[fvdb.GridBatch, fvdb.JaggedTensor]:
+    """Voxelize a mesh and interpolate per-corner values onto the resulting voxels.
+
+    Every input is jagged over the mesh's groups, and each group becomes one
+    grid in the returned :class:`fvdb.GridBatch`. Values live on face corners,
+    three independent values per triangle, so an attribute may be discontinuous
+    across an edge. They are interpolated to voxel centers by replaying the
+    stratified barycentric lattice that :meth:`fvdb.GridBatch.from_mesh` uses
+    internally, so every voxel is reached exactly once with barycentric
+    coordinates intact. Each voxel takes the value from the sample nearest its
+    center.
+
+    Args:
+        vertices: Group-local world-space vertex positions, jagged shape:
+            (G, V_g, 3). G: number of groups; V_g: vertices in group g.
+        faces: Triangle corner indices into the group's own vertices, jagged
+            shape: (G, F_g, 3). F_g: triangles in group g.
+        values: Per-corner attribute values to interpolate, jagged shape:
+            (G, F_g, 3, D), aligned corner for corner with ``faces``.
+            D: attribute dimensionality.
+        voxel_size: Voxel edge length in world units. Either a scalar applied
+            to every group, or a 1-D tensor of shape (G,) for per-group sizing.
+
+    Returns:
+        A 2-tuple of:
+
+        - :class:`fvdb.GridBatch` with one grid per group built from the mesh
+          surface.
+        - :class:`fvdb.JaggedTensor` of interpolated values, jagged shape
+          (G, M_g, D). M_g: number of voxels in group g's grid.
+    """
+    G = vertices.num_tensors
+    device = vertices.device
+
+    if torch.is_tensor(voxel_size):
+        sizes = [[float(voxel_size[g])] * 3 for g in range(G)]
+    else:
+        sizes = [[float(voxel_size)] * 3] * G
+
+    batch = fvdb.GridBatch.from_mesh(vertices, faces, voxel_sizes=sizes)
+
+    # Sample spacing in voxel units, from fVDB's IjkForMesh.cu.
+    spacing = math.sqrt(3.0) / 3.0
+
+    voxel_values: list[torch.Tensor] = []
+    for g in range(G):
+        grid = fvdb.Grid.from_grid_batch(batch, g)
+        group_vertices = vertices[g].jdata  # (V_g, 3)
+        group_faces = faces[g].jdata.long()  # (F_g, 3)
+        group_values = values[g].jdata  # (F_g, 3, D)
+
+        # Replay the stratified lattice fVDB used to decide which voxels exist,
+        # recovering barycentric coordinates for every sample.
+        corners = grid.world_to_voxel(group_vertices)[group_faces]  # (F_g, 3, 3)
+        origin = corners[:, 0]
+        edge_u = corners[:, 1] - origin
+        edge_v = corners[:, 2] - origin
+        num_u = torch.ceil(
+            ((edge_u * edge_u).sum(-1).sqrt() + spacing) / spacing
+        ).long()
+        num_v = torch.ceil(
+            ((edge_v * edge_v).sum(-1).sqrt() + spacing) / spacing
+        ).long()
+
+        offsets = torch.cat(
+            [torch.zeros(1, dtype=torch.long, device=device), (num_u * num_v).cumsum(0)]
+        )
+        sample = torch.arange(int(offsets[-1]), device=device)
+        triangle = torch.searchsorted(offsets, sample, right=True) - 1
+        within = sample - offsets[triangle]
+        rows = num_v[triangle]
+        i = within // rows
+        j = within - i * rows
+
+        u = i.float() / torch.clamp(num_u[triangle] - 1, min=1).float()
+        v = j.float() / torch.clamp(rows - 1, min=1).float()
+        folded = (u + v) >= 1.0
+        u = torch.where(folded, 1.0 - u, u)
+        v = torch.where(folded, 1.0 - v, v)
+
+        voxel_space = (
+            origin[triangle]
+            + edge_u[triangle] * u[:, None]
+            + edge_v[triangle] * v[:, None]
+        )
+        index = grid.ijk_to_index(torch.floor(voxel_space + 0.5).int())
+        inside = index >= 0
+
+        barycentric = torch.stack([1.0 - u - v, u, v], dim=-1)  # (S, 3)
+        interpolated = (barycentric[..., None] * group_values[triangle]).sum(
+            1
+        )  # (S, D)
+
+        # Per voxel, keep the sample nearest the voxel center.
+        centers = grid.voxel_to_world(grid.ijk.float())
+        diff = grid.voxel_to_world(voxel_space[inside]) - centers[index[inside]]
+        distance = (diff * diff).sum(-1).sqrt()
+        nearest = torch.full((grid.num_voxels,), float("inf"), device=device)
+        _ = nearest.scatter_reduce_(
+            0, index[inside], distance, reduce="amin", include_self=True
+        )
+
+        winner = torch.zeros(grid.num_voxels, dtype=torch.long, device=device)
+        candidates = torch.nonzero(inside).squeeze(1)
+        won = distance <= nearest[index[inside]] + 1e-9
+        _ = winner.scatter_(0, index[inside][won], candidates[won])
+        voxel_values.append(interpolated[winner])
+
+    return batch, fvdb.JaggedTensor(voxel_values)
+
+
+def estimate_voxel_size(
+    vertices: fvdb.JaggedTensor,
+    faces: fvdb.JaggedTensor,
+    detail_scale: float = 1.0,
+    percentile: float = 10,
+    smallest: float | None = None,
+    largest: float | None = None,
+) -> torch.Tensor:
+    """Estimate a voxel size per group from triangle edge lengths.
+
+    The estimate is driven by the ``percentile``-th triangle edge length scaled
+    by ``detail_scale``. A modeller subdivides where there is detail, so the
+    short edges track the finest tessellation present.
+
+    Args:
+        vertices: Group-local world-space vertex positions, jagged shape:
+            (G, V_g, 3). G: number of groups; V_g: vertices in group g.
+        faces: Triangle corner indices into the group's own vertices, jagged
+            shape: (G, F_g, 3). F_g: triangles in group g.
+        detail_scale: Voxel size as a multiple of the ``percentile``-th edge
+            length. Smaller oversamples; larger blurs.
+        percentile: Which edge-length percentile drives the base estimate.
+        smallest: Absolute floor on the voxel size in world units. No floor
+            applied when ``None``.
+        largest: Absolute ceiling on the voxel size in world units. No ceiling
+            applied when ``None``.
+
+    Returns:
+        Estimated voxel size per group in world units, shape: (G,).
+    """
+
+    def _size_for(g: int) -> torch.Tensor:
+        c = vertices[g].jdata[faces[g].jdata.long()]  # (F_g, 3, 3)
+        edges = torch.cat([c[:, 1] - c[:, 0], c[:, 2] - c[:, 1], c[:, 0] - c[:, 2]])
+        lengths = (edges * edges).sum(dim=-1).sqrt()  # (3 * F_g,)
+        s = detail_scale * torch.quantile(lengths, percentile / 100.0)
+        if smallest is not None:
+            s = s.clamp(min=smallest)
+        if largest is not None:
+            s = s.clamp(max=largest)
+        return s
+
+    return torch.stack([_size_for(g) for g in range(vertices.num_tensors)])
+
+
+def voxel_centers(topology: fvdb.GridBatch) -> fvdb.JaggedTensor:
+    """World-space center of every voxel in the batch.
+
+    Args:
+        topology: Voxelized geometry, one grid per group. G: number of grids.
+
+    Returns:
+        Voxel centers in world units, jagged shape: (G, M_g, 3). M_g: voxels in
+        grid g.
+    """
+    return fvdb.JaggedTensor(
+        [
+            grid.voxel_to_world(grid.ijk.float())
+            for grid in (
+                fvdb.Grid.from_grid_batch(topology, g)
+                for g in range(topology.grid_count)
+            )
+        ]
+    )
+
+
+def broadcast_to_voxels(
+    topology: fvdb.GridBatch, values: torch.Tensor
+) -> fvdb.JaggedTensor:
+    """Give every voxel of a grid the value belonging to that grid.
+
+    The trace reports which voxel a ray hit, so anything the caller knows per
+    group -- a material id, a part label -- has to be resolved per voxel to be
+    looked up with it.
+
+    Args:
+        topology: Voxelized geometry, one grid per group. G: number of grids.
+        values: One value per grid, shape: (G,) or (G, D). D: value
+            dimensionality.
+
+    Returns:
+        The grid's value repeated over its voxels, jagged shape: (G, M_g) or
+        (G, M_g, D). M_g: voxels in grid g.
+    """
+    return fvdb.JaggedTensor(
+        [
+            values[g].expand(topology.num_voxels_at(g), *values.shape[1:]).contiguous()
+            for g in range(topology.grid_count)
+        ]
+    )
diff --git a/modsim_toolbox/uv.lock b/modsim_toolbox/uv.lock
new file mode 100644
index 0000000..38fe31b
--- /dev/null
+++ b/modsim_toolbox/uv.lock
@@ -0,0 +1,475 @@
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