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/ .

翻译：我们提出了一种高效方法，可从多视角图像观测中联合优化拓扑、材质与光照。与近期通常生成编码于神经网络中的纠缠式三维表示的多视角重建方法不同，我们输出带有空间变化材质和环境光照的三角网格，可直接部署于任意传统图形引擎而无需修改。我们利用可微分渲染和基于坐标网络的最新成果紧凑表示体积纹理化，并结合可微分行进四面体算法，直接在表面网格上进行梯度优化。最后，我们引入了环境光照分裂和近似法的可微分公式化，以高效恢复全频光照。实验表明，基于三角网格渲染器（光栅化器与路径追踪器），我们提取的模型能以交互速率实现高级场景编辑、材质分解及高质量视角插值。项目网站：https://nvlabs.github.io/nvdiffrec/。