English

Proxy Tracing: Unbiased Reciprocal Estimation for Optimized Sampling in BDPT

Graphics 2025-04-14 v1

Abstract

Robust light transport algorithms, particularly bidirectional path tracing (BDPT), face significant challenges when dealing with specular or highly glossy involved paths. BDPT constructs the full path by connecting sub-paths traced individually from the light source and camera. However, it remains difficult to sample by connecting vertices on specular and glossy surfaces with narrow-lobed BSDF, as it poses severe constraints on sampling in the feasible direction. To address this issue, we propose a novel approach, called \emph{proxy sampling}, that enables efficient sub-path connection of these challenging paths. When a low-contribution specular/glossy connection occurs, we drop out the problematic neighboring vertex next to this specular/glossy vertex from the original path, then retrace an alternative sub-path as a proxy to complement this incomplete path. This newly constructed complete path ensures that the connection adheres to the constraint of the narrow lobe within the BSDF of the specular/glossy surface. Unbiased reciprocal estimation is the key to our method to obtain a probability density function (PDF) reciprocal to ensure unbiased rendering. We derive the reciprocal estimation method and provide an efficiency-optimized setting for efficient sampling and connection. Our method provides a robust tool for substituting problematic paths with favorable alternatives while ensuring unbiasedness. We validate this approach in the probabilistic connections BDPT for addressing specular-involved difficult paths. Experimental results have proved the effectiveness and efficiency of our approach, showcasing high-performance rendering capabilities across diverse settings.

Keywords

Cite

@article{arxiv.2503.23412,
  title  = {Proxy Tracing: Unbiased Reciprocal Estimation for Optimized Sampling in BDPT},
  author = {Fujia Su and Bingxuan Li and Qingyang Yin and Yanchen Zhang and Sheng Li},
  journal= {arXiv preprint arXiv:2503.23412},
  year   = {2025}
}
R2 v1 2026-06-28T22:39:31.484Z