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Robust Computation of Boundary Path Integrals Using Kernel-Density Estimation

DescriptionWe present a simple, robust, and consistent estimator for boundary path integrals in physics-based differentiable rendering. By reformulating boundary integration with kernel-density estimation, our method avoids fragile measure-zero sampling and costly reparameterization. It matches finite-difference derivatives and outperforms state-of-the-art baselines in synthetic inverse-rendering benchmarks with improved efficiency and lower variance.

Authors

Peiyu Xu

University of Illinois Urbana-Champaign

Lifan Wu

NVIDIA