标题： 辐射反向传播与非静态几何 显示英文标题

标题： Radiative Backpropagation with Non-Static Geometry

摘要： 基于辐射反向传播（RB）的方法通过模拟微分辐射亮度的传播，高效地计算物理基础可微渲染中的反向模式导数。 一个关键假设是微分辐射亮度像正常辐射亮度一样传输。 我们观察到，这仅在场景几何静态时成立，并且当场景参数改变几何时，当前的辐射反向传播实现会产生有偏梯度。 在本工作中，我们推导了不假设静态几何的微分传输方程。 直接结果是，当采样过程未被微分时，参数化很重要：只有表面积分允许导数的局部公式，即移动表面不影响整个路径几何的公式。 虽然已经投入了大量努力来处理由移动几何引起的不连续性，但我们表明，有偏的内部导数即使在最简单的逆向渲染任务中也会受到影响，无论是否存在不连续性。 基于我们推导的实现，在相同设置下会导致系统收敛到参考解，并为路径空间可微渲染提供无偏的RB内部导数。 显示英文摘要

摘要： Radiative backpropagation-based (RB) methods efficiently compute reverse-mode derivatives in physically-based differentiable rendering by simulating the propagation of differential radiance. A key assumption is that differential radiance is transported like normal radiance. We observe that this holds only when scene geometry is static and demonstrate that current implementations of radiative backpropagation produce biased gradients when scene parameters change geometry. In this work, we derive the differential transport equation without assuming static geometry. An immediate consequence is that the parameterization matters when the sampling process is not differentiated: only surface integrals allow a local formulation of the derivatives, i.e., one in which moving surfaces do not affect the entire path geometry. While considerable effort has been devoted to handling discontinuities resulting from moving geometry, we show that a biased interior derivative compromises even the simplest inverse rendering tasks, regardless of discontinuities. An implementation based on our derivation leads to systematic convergence to the reference solution in the same setting and provides unbiased RB interior derivatives for path-space differentiable rendering.