Name
Abstract | ||
|---|---|---|
In this paper, we propose two methods for estimating the scales of point clouds to align them. The first method estimates the scale of each point cloud separately: each point cloud has its own scale that is something like the size of a scene. We call it a keyscale, which is a representative scale and is defined for a given 3D point cloud as the minimum of the cumulative contribution rates of PCA of descriptors over different scales. Our second method directly estimates the ratio of scales (scale ratio) of two point clouds. Instead of finding the minimum, this approach registers the two sets of curves of the cumulative contribution rate of PCA by assuming that those differ only in scale. Experimental results with simulated and real scene point clouds demonstrate that the scale alignment of 3D point clouds can be effectively accomplished by our scale ratio estimation. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/s00138-014-0633-2 | Machine Vision and Applications |
Keywords | Field | DocType |
Scale,ICP,3D point cloud,Spin images,Registration | Pattern recognition,Computer science,Artificial intelligence,Point cloud | Journal |
Volume | Issue | ISSN |
25 | 8 | 0932-8092 |
Citations | PageRank | References |
6 | 0.45 | 20 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Baowei Lin | 1 | 12 | 3.27 |
| Toru Tamaki | 2 | 120 | 30.21 |
| Fangda Zhao | 3 | 7 | 1.17 |
| Bisser Raytchev | 4 | 212 | 33.11 |
| Kazufumi Kaneda | 5 | 439 | 86.44 |
| Koji Ichii | 6 | 12 | 2.30 |