Proxy Tracing: Unbiased Reciprocal Estimation for Optimized Sampling in BDPT
Fujia Su, Bingxuan Li, Qingyang Yin, Yanchen Zhang, Sheng Li
摘要
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 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.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
它引用的顶会 Paper6
- Specular manifold sampling for rendering high-frequency caustics and glintsTizian Zeltner, Iliyan Georgiev, Wenzel JakobSIGGRAPH 2020 · 被引用 49 次
- Continuous multiple importance samplingRex West, Iliyan Georgiev, Adrien Gruson, Toshiya HachisukaSIGGRAPH 2020 · 被引用 42 次
- Variance-aware path guidingAlexander Rath, Pascal Grittmann, Sebastian Herholz, Petr Vévoda 等SIGGRAPH 2020 · 被引用 39 次
- Unbiased and consistent rendering using biased estimatorsZackary Misso, Benedikt Bitterli, Iliyan Georgiev, Wojciech JaroszSIGGRAPH 2022 · 被引用 22 次
- EARS: efficiency-aware russian roulette and splittingAlexander Rath, Pascal Grittmann, Sebastian Herholz, Philippe Weier 等SIGGRAPH 2022 · 被引用 19 次
相关 Paper
- SPCBPT: subspace-based probabilistic connections for bidirectional path tracingFujia Su, Sheng Li, Guoping WangSIGGRAPH 2022 · 被引用 9 次
- Antithetic sampling for Monte Carlo differentiable renderingCheng Zhang, Zhao Dong, Michael C. Doggett, Shuang ZhaoSIGGRAPH 2021 · 被引用 55 次
- Segment-based Light Transport SimulationWenyou Wang, Rex West, Toshiya HachisukaSIGGRAPH 2025
- Quadric-Based Silhouette Sampling for Differentiable RenderingMariia Soroka, Christoph Peters, Steve MarschnerSIGGRAPH 2025 · 被引用 2 次
- Reconstructing Translucent Objects using Differentiable RenderingXi Deng, Fujun Luan, Bruce Walter, Kavita Bala 等SIGGRAPH 2022 · 被引用 30 次
