Coverage Axis++: Efficient Inner Point Selection for 3D Shape Skeletonization
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866929379706142720 |
|---|---|
| author | Wang, Zimeng Dou, Zhiyang Xu, Rui Lin, Cheng Liu, Yuan Long, Xiaoxiao Xin, Shiqing Komura, Taku Yuan, Xiaoming Wang, Wenping |
| author_facet | Wang, Zimeng Dou, Zhiyang Xu, Rui Lin, Cheng Liu, Yuan Long, Xiaoxiao Xin, Shiqing Komura, Taku Yuan, Xiaoming Wang, Wenping |
| contents | We introduce Coverage Axis++, a novel and efficient approach to 3D shape skeletonization. The current state-of-the-art approaches for this task often rely on the watertightness of the input or suffer from substantial computational costs, thereby limiting their practicality. To address this challenge, Coverage Axis++ proposes a heuristic algorithm to select skeletal points, offering a high-accuracy approximation of the Medial Axis Transform (MAT) while significantly mitigating computational intensity for various shape representations. We introduce a simple yet effective strategy that considers shape coverage, uniformity, and centrality to derive skeletal points. The selection procedure enforces consistency with the shape structure while favoring the dominant medial balls, which thus introduces a compact underlying shape representation in terms of MAT. As a result, Coverage Axis++ allows for skeletonization for various shape representations (e.g., water-tight meshes, triangle soups, point clouds), specification of the number of skeletal points, few hyperparameters, and highly efficient computation with improved reconstruction accuracy. Extensive experiments across a wide range of 3D shapes validate the efficiency and effectiveness of Coverage Axis++. Our codes are available at https://github.com/Frank-ZY-Dou/Coverage_Axis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_12946 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Coverage Axis++: Efficient Inner Point Selection for 3D Shape Skeletonization Wang, Zimeng Dou, Zhiyang Xu, Rui Lin, Cheng Liu, Yuan Long, Xiaoxiao Xin, Shiqing Komura, Taku Yuan, Xiaoming Wang, Wenping Computer Vision and Pattern Recognition Computational Geometry Graphics We introduce Coverage Axis++, a novel and efficient approach to 3D shape skeletonization. The current state-of-the-art approaches for this task often rely on the watertightness of the input or suffer from substantial computational costs, thereby limiting their practicality. To address this challenge, Coverage Axis++ proposes a heuristic algorithm to select skeletal points, offering a high-accuracy approximation of the Medial Axis Transform (MAT) while significantly mitigating computational intensity for various shape representations. We introduce a simple yet effective strategy that considers shape coverage, uniformity, and centrality to derive skeletal points. The selection procedure enforces consistency with the shape structure while favoring the dominant medial balls, which thus introduces a compact underlying shape representation in terms of MAT. As a result, Coverage Axis++ allows for skeletonization for various shape representations (e.g., water-tight meshes, triangle soups, point clouds), specification of the number of skeletal points, few hyperparameters, and highly efficient computation with improved reconstruction accuracy. Extensive experiments across a wide range of 3D shapes validate the efficiency and effectiveness of Coverage Axis++. Our codes are available at https://github.com/Frank-ZY-Dou/Coverage_Axis. |
| title | Coverage Axis++: Efficient Inner Point Selection for 3D Shape Skeletonization |
| topic | Computer Vision and Pattern Recognition Computational Geometry Graphics |
| url | https://arxiv.org/abs/2401.12946 |