让光线追踪可微分，同时保留偏振信息以提升逆渲染精度
Differentiable Polarized Path Tracing
- 结合路径重播与局部缓存，实现偏振光的稳定反向传播
- 在复杂场景中成功优化材质与光照参数，结果更准确
- 适合需要高精度几何和材质重建的研究者
基于物理的可微分渲染在逆渲染问题（如3D重建、反射率估计、光照估计）中表现优异，但现有方法仅依赖辐射强度，忽略了能约束场景几何与材质属性的偏振信息。虽然偏振光的前向模拟可通过穆勒-斯托克斯理论明确定义，但将其扩展至反向传播面临重大挑战：常见偏振算子（如线性偏振片、漫反射）的秩亏性质违反了标准梯度估计器（如路径重播反向传播）的可逆性假设，导致数值不稳定。本文提出一种鲁棒的偏振感知可微分路径追踪方法，通过路径重播与局部缓存的结合，估计无偏梯度。该方法实现了复杂场景中材质与光照参数的高效且稳定的优化，显著拓展了基于物理的逆渲染的应用范围。
原文摘要 · Abstract (English)
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/
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