发表机构
Max Planck Institute for Informatics; Saarland Informatics Campus; VIA Research Center; Google（马克斯·普朗克信息研究所; 萨尔兰信息学园区; VIA研究中心; 谷歌）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究逆渲染问题，提出偏振感知的可微路径追踪方法，通过路径重放和局部缓存组合估计无偏梯度，能在复杂场景中高效稳定优化材质和光照参数，拓宽基于物理的逆渲染适用性。
AI 中文摘要
基于物理的可微渲染已被证明是解决逆渲染问题（如三维重建、反射率估计、光照估计）的有力工具。然而，大多数现有方法仅对辐射强度进行操作，丢弃了约束场景几何和材质属性的宝贵偏振线索。虽然通过穆勒-斯托克斯微积分对偏振光进行正向模拟是明确的，但将反向模式微分扩展到该领域面临重大挑战。常见偏振算子（如线性偏振器和漫反射）的秩亏性质违反了路径重放反向传播等标准梯度估计器的可逆性假设，导致数值不稳定。我们提出了一种强大的、偏振感知的可微路径追踪方法来解决此问题。我们的方法通过路径重放和局部缓存的组合来估计无偏梯度。这种公式化使得在复杂场景中对材质和光照参数进行高效稳定的优化成为可能，拓宽了基于物理的逆渲染的适用性。
英文摘要
Physically based differentiable rendering has proven to be a powerful tool for inverse rendering problems (e.g., 3D reconstruction, reflectance estimation, lighting estimation). However, most existing methods operate solely on radiometric intensity, discarding valuable polarization cues that constrain scene geometry and material properties. While forward simulation of polarized light is well-defined via Mueller-Stokes calculus, extending reverse-mode differentiation to this domain presents significant challenges. The rank-deficient nature of common polarimetric operators, such as linear polarizers and diffuse reflections, violates the invertibility assumptions of standard gradient estimators like path replay backpropagation and results in numerical instability. We address this by proposing a robust, polarization-aware differentiable path tracing method. Our approach estimates unbiased gradients through a combination of path replay and local caching. This formulation enables efficient and stable optimization of material and lighting parameters in complex scenes, broadening the applicability of physically based inverse rendering. Project page: https://vcai.mpi-inf.mpg.de/projects/DPPT/
CommentsAccepted at ECCV 2026
