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arXiv · 2111.12503

Extracting Triangular 3D Models, Materials, and Lighting From Images

Jacob Munkberg·Jon Hasselgren·Tianchang Shen·Jun Gao·Wenzheng Chen·Alex Evans·Thomas Müller·Sanja Fidler
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Abstract

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/ .

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Jacob Munkberg, Jon Hasselgren, Tianchang Shen, Jun Gao, Wenzheng Chen, Alex Evans, Thomas Müller, Sanja Fidler. 2021-11-24. Extracting Triangular 3D Models, Materials, and Lighting From Images. https://arxiv.org/abs/2111.12503

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