# Coverage Axis: Inner Point Selection for 3D Shape Skeletonization

arXiv: https://arxiv.org/abs/2110.00965 · HTML: https://mlsanity.com/paper/2110.00965/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2021.

## Built on by (most cited first, 22 of 22)

- [Part123: Part-aware 3D Reconstruction from a Single-view Image](https://mlsanity.com/paper/2405.16888/) (2024)
- [Zolly: Zoom Focal Length Correctly for Perspective-Distorted Human Mesh Reconstruction](https://mlsanity.com/paper/2303.13796/) (2023)
- [Scalable Video Object Segmentation with Simplified Framework](https://mlsanity.com/paper/2308.09903/) (2023)
- [CASSPR: Cross Attention Single Scan Place Recognition](https://mlsanity.com/paper/2211.12542/) (2022)
- [TetSphere Splatting: Representing High-Quality Geometry with Lagrangian Volumetric Meshes](https://mlsanity.com/paper/2405.20283/) (2024)
- [RigGS: Rigging of 3D Gaussians for Modeling Articulated Objects in Videos](https://mlsanity.com/paper/2503.16822/) (2025)
- [GEM3D: GEnerative Medial Abstractions for 3D Shape Synthesis](https://mlsanity.com/paper/2402.16994/) (2024)
- [Coverage Axis++: Efficient Inner Point Selection for 3D Shape Skeletonization](https://mlsanity.com/paper/2401.12946/) (2024)
- [CWF: Consolidating Weak Features in High-quality Mesh Simplification](https://mlsanity.com/paper/2404.15661/) (2024)
- [Learning Dense UV Completion for Human Mesh Recovery](https://mlsanity.com/paper/2307.11074/) (2023)
- [Deep3DSketch+: Rapid 3D Modeling from Single Free-hand Sketches](https://mlsanity.com/paper/2309.13006/) (2023)
- [EPCS: Endpoint-based Part-aware Curve Skeleton Extraction for Low-quality Point Clouds](https://mlsanity.com/paper/2211.09488/) (2022)
- [The Sum of Its Parts: Visual Part Segmentation for Inertial Parameter Identification of Manipulated Objects](https://mlsanity.com/paper/2302.06685/) (2023)
- [Dynamic Realms: 4D Content Analysis, Recovery and Generation with Geometric, Topological and Physical Priors](https://mlsanity.com/paper/2409.14692/) (2024)
- [Deep Medial Voxels: Learned Medial Axis Approximations for Anatomical Shape Modeling](https://mlsanity.com/paper/2403.11790/) (2024)
- [MorphoSkel3D: Morphological Skeletonization of 3D Point Clouds for Informed Sampling in Object Classification and Retrieval](https://mlsanity.com/paper/2501.12974/) (2025)
- [SkeletonGaussian: Editable 4D Generation through Gaussian Skeletonization](https://mlsanity.com/paper/2602.04271/) (2026)
- [SHARC: Reference point driven Spherical Harmonic Representation for Complex Shapes](https://mlsanity.com/paper/2604.01894/) (2026)
- [GaussiAnimate: Rig Animatable Categories with Level of Dynamics](https://mlsanity.com/paper/2604.08547/) (2026)
- [Soft Anisotropic Diagrams for Differentiable Image Representation](https://mlsanity.com/paper/2604.21984/) (2026)
- [Curve Skeletonization in Continuous domain for Meshes and Point Clouds](https://mlsanity.com/paper/2605.25921/) (2026)
- [HoloTetSphere: Unified TetSphere Mesh Reconstruction for Physical Simulations](https://mlsanity.com/paper/2607.08398/) (2026)

## Abstract (arXiv)

In this paper, we present a simple yet effective formulation called Coverage Axis for 3D shape skeletonization. Inspired by the set cover problem, our key idea is to cover all the surface points using as few inside medial balls as possible. This formulation inherently induces a compact and expressive approximation of the Medial Axis Transform (MAT) of a given shape. Different from previous methods that rely on local approximation error, our method allows a global consideration of the overall shape structure, leading to an efficient high-level abstraction and superior robustness to noise. Another appealing aspect of our method is its capability to handle more generalized input such as point clouds and poor-quality meshes. Extensive comparisons and evaluations demonstrate the remarkable effectiveness of our method for generating compact and expressive skeletal representation to approximate the MAT.