Inverse path tracing has recently been applied to joint material and lighting estimation, given geometry and multi-view HDR observations of an indoor scene. However, it has two major limitations: path tracing is expensive to compute, and ambiguities exist between reflection and emission. We propose a novel Factorized Inverse Path Tracing (FIPT) method which utilizes a factored light transport formulation and finds emitters driven by rendering errors. Our algorithm enables accurate material and lighting optimization faster than previous work, and is more effective at resolving ambiguities. The exhaustive experiments on synthetic scenes show that our method (1) outperforms state-of-the-art indoor inverse rendering and relighting methods particularly in the presence of complex illumination effects; (2) speeds up inverse path tracing optimization to less than an hour. We further demonstrate robustness to noisy inputs through material and lighting estimates that allow plausible relighting in a real scene. The source code is available at: https://github.com/lwwu2/fipt

翻译：反向路径追踪近期已被应用于室内场景的联合材质与光照估计，其输入包括几何信息及多视角高动态范围观测数据。然而，该方法存在两大局限性：路径追踪计算成本高昂，且反射与自发光之间存在模糊性。我们提出了一种新颖的因子化反向路径追踪方法，该方法采用因子化光传输公式，并通过渲染误差驱动发射体的定位。相较于前人工作，我们的算法能实现更精准的材质与光照优化，且效率更高，在消除模糊性方面也更具成效。在合成场景上的详尽实验表明，我们的方法：（1）显著优于现有最先进的室内逆渲染与重光照方法，尤其在处理复杂光照效果时；（2）将反向路径追踪优化速度提升至不足一小时。我们进一步通过对真实场景中噪声输入的材质与光照估计，证明了方法的鲁棒性，可实现可信的重光照效果。源代码获取地址：https://github.com/lwwu2/fipt