SDFNet aims to go from boring flat, 2D images to less boring Signed Distance Functions for representing them in 3D, for simple, symmetric shapes. It uses Neural Radiance Fields (NeRFs) and Constructive Solid Geometry to go from images to 3-Dimensional objects, to SDFs for the shapes.
We're deeply referencing the code accompanying the paper: CSGNet: Neural Shape Parser for Constructive Solid Geometry, CVPR 2018. Also referenced is the paper on Neural Radiance Fields, whose code can be found here.
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- The report for the work done on this project in Sem 1 [AY 2022-23] can be found here.
- All other deliverables can be found here.
The final poster for the work done in the first half of the project:
To ask questions, please email any one of us: Aniket, Progyan and Shruhrid.