Computer Science > Computer Vision and Pattern Recognition
arXiv:2603.09285 (cs)
[Submitted on 10 Mar 2026]
Title:Learning Convex Decomposition via Feature Fields
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Abstract:This work proposes a new formulation to the long-standing problem of convex decomposition through learning feature fields, enabling the first feed-forward model for open-world convex decomposition. Our method produces high-quality decompositions of 3D shapes into a union of convex bodies, which are essential to accelerate collision detection in physical simulation, amongst many other applications. The key insight is to adopt a feature learning approach and learn a continuous feature field that can later be clustered to yield a good convex decomposition via our self-supervised, purely-geometric objective derived from the classical definition of convexity. Our formulation can be used for single shape optimization, but more importantly, feature prediction unlocks scalable, self-supervised learning on large datasets resulting in the first learned open-world model for convex decomposition. Experiments show that our decompositions are higher-quality than alternatives and generalize across open-world objects as well as across representations to meshes, CAD models, and even Gaussian splats. this https URL
| Comments: | |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2603.09285 [cs.CV] |
| (or arXiv:2603.09285v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2603.09285
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View a PDF of the paper titled Learning Convex Decomposition via Feature Fields, by Yuezhi Yang and 3 other authors
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