Differentiable Heightfield Path Tracing with Accelerated Discontinuities
Xiaochun Tong, Hsueh-Ti Derek Liu, Yotam I. Gingold, Alec Jacobson
Abstract
We investigate the problem of accelerating a physically-based differentiable renderer for heightfields based on path tracing with global illumination. On a heightfield with 1 million vertices (1024 × 1024 resolution), our differentiable renderer requires only 4 ms per sample per pixel when differentiating direct illumination, orders of magnitude faster than most existing general 3D mesh differentiable renderers. It is well-known that one can leverage spatial hierarchical data structures (e.g., the maximum mipmaps) to accelerate the forward pass of heightfield rendering. The key idea of our approach is to further utilize the hierarchy to speed up the backward pass—differentiable heightfield rendering. Specifically, we use the maximum mipmaps to accelerate the process of identifying scene discontinuities, which is crucial for obtaining accurate derivatives. Our renderer supports global illumination. we are able to optimize global effects, such as shadows, with respect to the geometry and the material parameters. Our differentiable renderer achieves real-time frame rates and unlocks interactive inverse rendering applications. We demonstrate the flexibility of our method with terrain optimization, geometric illusions, shadow optimization, and text-based shape generation.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 01b836b0-1772-4e77-904b-e5c1847e0cb8Cited by top-tier papers1
Ask how each one uses itBuilds on12
- Learning Transferable Visual Models From Natural Language SupervisionAlec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh et al.ICML 2021 · 47,906 citations
- Soft Rasterizer: A Differentiable Renderer for Image-Based 3D ReasoningShichen Liu, Weikai Chen, Tianye Li, Hao LiICCV 2019 · 789 citations
- DR.JIT: a just-in-time compiler for differentiable renderingWenzel Jakob, Sébastien Speierer, Nicolas Roussel, Delio ViciniSIGGRAPH 2022 · 160 citations
- Learning Deformable Tetrahedral Meshes for 3D ReconstructionJun Gao, Wenzheng Chen, Tommy Xiang, Alec Jacobson et al.NeurIPS 2020 · 134 citations
- Radiative backpropagation: an adjoint method for lightning-fast differentiable renderingMerlin Nimier-David, Sébastien Speierer, Benoît Ruiz, Wenzel JakobSIGGRAPH 2020 · 107 citations
Related papers
- Monte Carlo estimators for differential light transportTizian Zeltner, Sébastien Speierer, Iliyan Georgiev, Wenzel JakobSIGGRAPH 2021 · 70 citations
- Antithetic sampling for Monte Carlo differentiable renderingCheng Zhang, Zhao Dong, Michael C. Doggett, Shuang ZhaoSIGGRAPH 2021 · 55 citations
- Path-space differentiable renderingCheng Zhang, Bailey Miller, Kai Yan, Ioannis Gkioulekas et al.SIGGRAPH 2020 · 155 citations
- Differentiable Shadow Mapping for Efficient Inverse GraphicsMarkus Worchel, Marc AlexaCVPR 2023
- Quadric-Based Silhouette Sampling for Differentiable RenderingMariia Soroka, Christoph Peters, Steve MarschnerSIGGRAPH 2025 · 2 citations
