Name
Abstract | ||
|---|---|---|
Consistent normal orientation is challenging in the presence of noise, non-uniformities and thin sharp features. None of any existing local or global methods is capable of orienting all point cloud models consistently, and none of them offers a mechanism to rectify the inconsistent normals. In this paper, we present a new normal orientation method based on the multi-source propagation technique with two insights: faithful normals respecting sharp features tend to cause incorrect orientation propagation, and propagation orientation just using one source is problematic. It includes a novel orientation-benefit normal estimation algorithm for reducing wrong normal propagation near sharp features, and a multi-source orientation propagation algorithm for orientation improvement. The results of any orientation methods can be corrected by adding more credible sources, interactively or automatically, then propagating. To alleviate the manual work of interactive orientation, we devise an automatic propagation source extraction method by visibility voting. It can be applied directly to find multiple credible sources, combining with our orientation-benefit normals and multi-source propagation technique, to generate a consistent orientation, or to rectify an inconsistent orientation. The experimental results show that our methods generate consistent orientation more or as faithful as those global methods with far less computational cost. Hence it is more pragmatic and suitable to handle large point cloud models. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.cad.2014.05.006 | Computer-Aided Design |
Keywords | Field | DocType |
orientation,point cloud,surface reconstruction | Surface reconstruction,Computer vision,Visibility,Mathematical optimization,Voting,Artificial intelligence,Point cloud,Normal estimation,Mathematics | Journal |
Volume | Issue | ISSN |
55 | 1 | 0010-4485 |
Citations | PageRank | References |
4 | 0.40 | 21 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jian Liu | 1 | 289 | 59.26 |
| Junjie Cao | 2 | 212 | 18.07 |
| Xiuping Liu | 3 | 156 | 18.74 |
| Jun Wang | 4 | 372 | 47.52 |
| Xiaochao Wang | 5 | 5 | 1.11 |
| Xiquan Shi | 6 | 93 | 12.31 |