We present an efficient method for joint optimization of topology, materials and lighting from multi-view image observations. Unlike recent multi-view reconstruction approaches, which typically produce entangled 3D representations encoded in neural networks, we output triangle meshes with spatially-varying materials and environment lighting that can be deployed in any traditional graphics engine unmodified. We leverage recent work in differentiable rendering, coordinate-based networks to compactly represent volumetric texturing, alongside differentiable marching tetrahedrons to enable gradient-based optimization directly on the surface mesh. Finally, we introduce a differentiable formulation of the split sum approximation of environment lighting to efficiently recover all-frequency lighting. Experiments show our extracted models used in advanced scene editing, material decomposition, and high quality view interpolation, all running at interactive rates in triangle-based renderers (rasterizers and path tracers). Project website: https://nvlabs.github.io/nvdiffrec/ .
@article{arxiv.2111.12503,
title = {Extracting Triangular 3D Models, Materials, and Lighting From Images},
author = {Jacob Munkberg and Jon Hasselgren and Tianchang Shen and Jun Gao and Wenzheng Chen and Alex Evans and Thomas Müller and Sanja Fidler},
journal= {arXiv preprint arXiv:2111.12503},
year = {2023}
}