Approximate convex decomposition for 3D meshes with collision-aware concavity and tree search
Xinyue Wei, Minghua Liu, Zhan Ling, Hao Su
Abstract
Approximate convex decomposition aims to decompose a 3D shape into a set of almost convex components, whose convex hulls can then be used to represent the input shape. It thus enables efficient geometry processing algorithms specifically designed for convex shapes and has been widely used in game engines, physics simulations, and animation. While prior works can capture the global structure of input shapes, they may fail to preserve fine-grained details (e.g., filling a toaster's slots), which are critical for retaining the functionality of objects in interactive environments. In this paper, we propose a novel method that addresses the limitations of existing approaches from three perspectives: (a) We introduce a novel collision-aware concavity metric that examines the distance between a shape and its convex hull from both the boundary and the interior. The proposed concavity preserves collision conditions and is more robust to detect various approximation errors. (b) We decompose shapes by directly cutting meshes with 3D planes. It ensures generated convex hulls are intersection-free and avoids voxelization errors. (c) Instead of using a one-step greedy strategy, we propose employing a multi-step tree search to determine the cutting planes, which leads to a globally better solution and avoids unnecessary cuttings. Through extensive evaluation on a large-scale articulated object dataset, we show that our method generates decompositions closer to the original shape with fewer components. It thus supports delicate and efficient object interaction in downstream applications.
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.
Cited by top-tier papers27
- Robust Low-Poly Meshing for General 3D ModelsZhen Chen, Zherong Pan, Kui Wu, Etienne Vouga et al.SIGGRAPH 2023 · 32 citations
- CAST: Component-Aligned 3D Scene Reconstruction from an RGB ImageKaixin Yao, Longwen Zhang, Xinhao Yan, Yan Zeng et al.SIGGRAPH 2025 · 30 citations
- Towards Physically Executable 3D Gaussian for Embodied NavigationBingchen Miao, Rong Wei, Zhiqi Ge, Xiaoquan sun et al.ICLR 2026 · 22 citations
- SceneSmith: Agentic Generation of Simulation-Ready Indoor ScenesNicholas Pfaff, Thomas Cohn, Sergey Zakharov, Rick Cory et al.ICML 2026 · 21 citations
- ManiSkill2: A Unified Benchmark for Generalizable Manipulation SkillsJiayuan Gu, Fanbo Xiang, Xuanlin Li, Zhan Ling et al.ICLR 2023 · 21 citations
Builds on7
- Learning Unsupervised Hierarchical Part Decomposition of 3D Objects From a Single RGB ImageDespoina Paschalidou, Luc Van Gool, Andreas GeigerCVPR 2020
- CvxNet: Learnable Convex DecompositionBoyang Deng, Kyle Genova, Soroosh Yazdani, Sofien Bouaziz et al.CVPR 2020
- SAPIEN: A SimulAted Part-Based Interactive ENvironmentFanbo Xiang, Yuzhe Qin, Kaichun Mo, Yikuan Xia et al.CVPR 2020
- Learning Generative Models of Shape HandlesMatheus Gadelha, Giorgio Gori, Duygu Ceylan, Radomír Mech et al.CVPR 2020
- Deep Parametric Shape Predictions Using Distance FieldsDmitriy Smirnov, Matthew Fisher, Vladimir G. Kim, Richard Zhang et al.CVPR 2020
Related papers
- Navigation-Driven Approximate Convex DecompositionJames AndrewsSIGGRAPH 2024 · 1 citation
- Learning Convex Decomposition via Feature FieldsYuezhi Yang, Qixing Huang, Mikaela Angelina Uy, Nicholas SharpCVPR 2026 · 2 citations
- TopoCut: fast and robust planar cutting of arbitrary domainsXianzhong Fang, Mathieu Desbrun, Hujun Bao, Jin HuangSIGGRAPH 2022 · 8 citations
- Low-poly Mesh Generation for Building ModelsXifeng Gao, Kui Wu, Zherong PanSIGGRAPH 2022 · 23 citations
- Learning Shape Primitives via Implicit Convexity RegularizationXiaoyang Huang, Yi Zhang, Kai Chen, Teng Li et al.ICCV 2023 · 6 citations
