Learning for 3D Vision

● Trained an encoder-decoder architecture for regressing voxels, point clouds and mesh representation from a single view RGB input.
● Implemented Nerf from scratch with Differentiable Renderer for emission-absorption volumes, uniform ray sampler and view dependent MLP with position encoding.
● Created a code base for sphere tracing for rendering Sign Distance Function and trained an ML architecture for neural SDF on point cloud data.

Learning Outcomes:

1. 3D Representations.
2. Single-view 3D: Objects.
3. Articulated Models - SMPL, SMPLX, Hands and animals.
4. Neural Radiance Fields (Nerf) and Volumetric Rendering.
5. Neural Surface Rendering.
6. Pointcloud - Classification, detection and Segmentation.

Programming Language: Pytorch, Pytorch3D, Python