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. Our Factorized Inverse Path Tracing (FIPT) addresses these challenges by using 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：逆路径追踪近年来被应用于室内场景的联合材质与光照估计，基于已知几何结构和多视角高动态范围观测数据。然而，该方法存在两大局限：路径追踪计算成本高昂，且反射与辐射之间存在歧义性。我们提出的因式分解逆路径追踪（Factorized Inverse Path Tracing, FIPT）通过采用因式分解光传输公式并利用渲染误差驱动发光体检测来应对这些挑战。该算法能够以优于先前工作的速度实现高精度材质与光照优化，并在消除歧义性方面更具成效。在合成场景上的全面实验表明，本方法：(1) 在复杂光照效应场景下显著优于现有室内逆渲染与重光照方法；(2) 将逆路径追踪优化速度提升至一小时以内。我们进一步通过对真实场景中材质与光照估计结果的鲁棒性验证，证明该方法可对含噪声输入实现可信的重光照效果。源代码已开源至：https://github.com/lwwu2/fipt