Differentiable Voxelization of Surface Representations
Tobias Djuren, Ugo Paavo Finnendahl, Markus Worchel, Hendrik Meyer, Marc Alexa
Abstract
Different shape representations facilitate different computations. Surface representations, in particular meshes, are often used for modeling, whereas volume representations are useful for spatial queries such as intersection or containment. Optimizing a surface representation based on a volumetric properties by gradient descent requires the derivatives of the volume relative to its bounding surface. We derive this gradient for winding numbers and show that it can be efficiently computed for volumetric values sampled on a regular grid (voxel representation) and surface parameters based on vertex sets (triangle meshes). This enables an efficient solution for a variety of optimization problems. We demonstrate the practical use of this approach at the examples of deforming meshes to resolve intersections, being manufacturable by cutting with a bandsaw from three directions, and creating shapes that are close to tiling 3D space.
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 24899fb4-2d0a-4006-82a4-1f009e11c8e7Builds on18
- NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view ReconstructionPeng Wang, Lingjie Liu, Yuan Liu, Christian Theobalt et al.NeurIPS 2021 · 2,500 citations
- Neural Sparse Voxel FieldsLingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua et al.NeurIPS 2020 · 1,535 citations
- Voxel R-CNN: Towards High Performance Voxel-based 3D Object DetectionJiajun Deng, Shaoshuai Shi, Peiwei Li, Wengang Zhou et al.AAAI 2021 · 1,128 citations
- Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields ReconstructionCheng Sun, Min Sun, Hwann-Tzong ChenCVPR 2022 · 859 citations
- Deep Marching Tetrahedra: a Hybrid Representation for High-Resolution 3D Shape SynthesisTianchang Shen, Jun Gao, Kangxue Yin, Ming-Yu Liu et al.NeurIPS 2021 · 652 citations
Related papers
- VoroMesh: Learning Watertight Surface Meshes with Voronoi DiagramsNissim Maruani, Roman Klokov, Maks Ovsjanikov, Pierre Alliez et al.ICCV 2023 · 28 citations
- Spatially Accelerated Winding Numbers for Curved GeometryJacob Spainhour, Brad Whitlock, Kenneth WeissSIGGRAPH 2026
- Mesh Splatting for End-to-end Multiview Surface ReconstructionRuiqi Zhang, Jiacheng Wu, Jie ChenICLR 2026
- TetWeave: Isosurface Extraction using On-The-Fly Delaunay Tetrahedral Grids for Gradient-Based Mesh OptimizationAlexandre Binninger, Ruben Wiersma, Philipp Herholz, Olga Sorkine-HornungSIGGRAPH 2025 · 10 citations
- DMesh: A Differentiable Mesh RepresentationSanghyun Son, Matheus Gadelha, Yang Zhou, Zexiang Xu et al.NeurIPS 2024 · 11 citations
