Approximate Convex Decomposition for 3D Meshes with Collision-Aware Concavity and Tree Search
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Abstract (arXiv)
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. We will release our implementation to facilitate future research.
Key facts. Cited by 67 papers in the map (data through 14 Oct 2026). Builds on 13 of its 64 references that are in the map. First posted on arXiv 5 May 2022.
Builds on
13 of 64 references.
References · 13
- 2018Soft Actor-Critic Algorithms and Applicationscited by 1,240
- 2020SAPIEN: A SimulAted Part-based Interactive ENvironmentcited by 595
- 2019BSP-Net: Generating Compact Meshes via Binary Space Partitioningcited by 206
- 2016Learning Shape Abstractions by Assembling Volumetric Primitivescited by 181
- 2019CvxNet: Learnable Convex Decompositioncited by 174
- 2019Superquadrics Revisited: Learning 3D Shape Parsing beyond Cuboidscited by 124
- 2018Supervised Fitting of Geometric Primitives to 3D Point Cloudscited by 91
- 20173D-PRNN: Generating Shape Primitives with Recurrent Neural Networkscited by 77
- 2020Learning Unsupervised Hierarchical Part Decomposition of 3D Objects from a Single RGB Imagecited by 72
- 2018Robust Watertight Manifold Surface Generation Method for ShapeNet Modelscited by 59
- 2021DeepMetaHandles: Learning Deformation Meta-Handles of 3D Meshes with Biharmonic Coordinatescited by 37
- 2019Deep Parametric Shape Predictions using Distance Fieldscited by 30
1 more
- 2020Learning Generative Models of Shape Handlescited by 25
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Citing papers per year: 2022: 3; 2023: 8; 2024: 15; 2025: 16; 2026: 25.
67 papers.
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