A Geometric Algorithm for Blood Vessel Reconstruction from Skeletal Representation
Fuente:
arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866917392717709312 |
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| author | Zhang, Guoqing Li, Yang |
| author_facet | Zhang, Guoqing Li, Yang |
| contents | We introduce a novel approach for the reconstruction of tubular shapes from skeletal representations. Our method processes all skeletal points as a whole, eliminating the need for splitting input structure into multiple segments. We represent the tubular shape as a truncated signed distance function (TSDF) in a voxel hashing manner, in which the signed distance between a voxel center and the object is computed through a simple geometric algorithm. Our method does not involve any surface sampling scheme or solving large matrix equations, and therefore is a faster and more elegant solution for tubular shape reconstruction compared to other approaches. Experiments demonstrate the efficiency and effectiveness of the proposed method. Code is avaliable at https://github.com/wlsdzyzl/Dragon. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_12797 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Geometric Algorithm for Blood Vessel Reconstruction from Skeletal Representation Zhang, Guoqing Li, Yang Computer Vision and Pattern Recognition Computational Geometry We introduce a novel approach for the reconstruction of tubular shapes from skeletal representations. Our method processes all skeletal points as a whole, eliminating the need for splitting input structure into multiple segments. We represent the tubular shape as a truncated signed distance function (TSDF) in a voxel hashing manner, in which the signed distance between a voxel center and the object is computed through a simple geometric algorithm. Our method does not involve any surface sampling scheme or solving large matrix equations, and therefore is a faster and more elegant solution for tubular shape reconstruction compared to other approaches. Experiments demonstrate the efficiency and effectiveness of the proposed method. Code is avaliable at https://github.com/wlsdzyzl/Dragon. |
| title | A Geometric Algorithm for Blood Vessel Reconstruction from Skeletal Representation |
| topic | Computer Vision and Pattern Recognition Computational Geometry |
| url | https://arxiv.org/abs/2402.12797 |