Computing Smooth Quasi-geodesic Distance Field (QGDF) with Quadratic Programming
文献类型：会议论文
| 作者 | Cao, Luming; Zhao, Junhao; Xu, Jian; Chen, Shuangmin; Liu, Guozhu; Xin, Shiqing; Zhou, Yuanfeng; He, Ying |
| 出版日期 | 2020 |
| 会议日期 | JUN 02-04, 2020 |
| 关键词 | GRAPH COMPUTATION |
| 卷号 | 127 |
| DOI | 10.1016/j.cad.2020.102879 |
| 英文摘要 | Computing geodesic distances on polyhedral surfaces is an important task in digital geometry processing. Speed and accuracy are two commonly-used measurements of evaluating a discrete geodesic algorithm. In applications, such as parametrization and shape analysis, a smooth distance field is often preferred over the exact, non-smooth geodesic distance field. We use the term Quasi-geodesic Distance Field (QGDF) to denote a smooth scalar field that is as close as possible to an exact geodesic distance field. In this paper, we formulate the problem of computing QGDF into a standard quadratic programming (QP) problem which maintains a trade-off between accuracy and smoothness. The proposed QP formulation is also flexible in that it can be naturally extended to point clouds and tetrahedral meshes, and support various user-specified constraints. We demonstrate the effectiveness of QGDF in defect-tolerant distances and symmetry-constrained distances. (C) 2020 Elsevier Ltd. All rights reserved. |
| 学科主题 | Computer Science |
| ISSN号 | 0010-4485 |
| 源URL | [http://ir.nimte.ac.cn/handle/174433/23295]  |
| 专题 | 会议专题 会议专题_会议论文 |
| 推荐引用方式 GB/T 7714 | Cao, Luming,Zhao, Junhao,Xu, Jian,et al. Computing Smooth Quasi-geodesic Distance Field (QGDF) with Quadratic Programming[C]. 见:. JUN 02-04, 2020. |
入库方式： OAI收割
来源：宁波材料技术与工程研究所
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