4.7 Article Proceedings Paper

Computing Medial Axis Transform with Feature Preservation via Restricted Power Diagram

期刊

ACM TRANSACTIONS ON GRAPHICS
卷 41, 期 6, 页码 -

出版社

ASSOC COMPUTING MACHINERY
DOI: 10.1145/3550454.3555465

关键词

Medial Axis Transform; Feature Preservation; Restricted Power Diagram

资金

  1. National Science Foundation [OAC-2007661]
  2. National Key Research and Development Program of China [2020YFB1708900]

向作者/读者索取更多资源

This paper proposes a novel framework using restricted power diagram (RPD) to compute the medial axis transform of 3D shapes, while preserving both external and internal medial features. The RPD provides connectivity and tangential surface regions of medial spheres, allowing for the detection and preservation of insufficient sphere sampling around medial features. Compared to existing methods, this framework is able to preserve a higher quality of the medial mesh.
We propose a novel framework for computing the medial axis transform of 3D shapes while preserving their medial features via restricted power diagram (RPD). Medial features, including external features such as the sharp edges and corners of the input mesh surface and internal features such as the seams and junctions of medial axis, are important shape descriptors both topologically and geometrically. However, existing medial axis approximation methods fail to capture and preserve them due to the fundamentally undersampling in the vicinity of medial features, and the difficulty to build their correct connections. In this paper we use the RPD of medial spheres and its affiliated structures to help solve these challenges. The dual structure of RPD provides the connectivity of medial spheres. The surfacic restricted power cell (RPC) of each medial sphere provides the tangential surface regions that these spheres have contact with. The connected components (CC) of surfacic RPC give us the classification of each sphere, to be on a medial sheet, a seam, or a junction. They allow us to detect insufficient sphere sampling around medial features and develop necessary conditions to preserve them. Using this RPD-based framework, we are able to construct high quality medial meshes with features preserved. Compared with existing sampling-based or voxel-based methods, our method is the first one that can preserve not only external features but also internal features of medial axes.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据