This repository implements joint optimization of 3D topology, materials, and lighting from images using differentiable rendering. It uses PyTorch for deep learning components and adapts Kaolin’s differentiable marching tetrahedons implementation. The method supports advanced isosurfacing via FlexiCubes and offers a slangpy-based autodiff implementation for simplified code.