Name
Abstract | ||
|---|---|---|
The Iterative Closest Point (ICP) algorithm is a widely used method for aligning three-dimensional point sets. The quality of alignment obtained by this algorithm depends heavily on choosing good pairs of corresponding points in the two datasets. If too many points are chosen from featureless regions of the data, the algorithm converges slowly, finds the wrong pose, or even diverges, especially in the presence of noise or miscalibration in the input data. In this paper we describe a method for detecting uncertainty in pose, and we propose a point selection strategy for ICP that minimizes this uncertainty by choosing samples that constrain potentially unstable transformations. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1109/IM.2003.1240258 | FOURTH INTERNATIONAL CONFERENCE ON 3-D DIGITAL IMAGING AND MODELING, PROCEEDINGS |
Keywords | Field | DocType |
iterative methods,three dimensional,mesh generation,iterative closest point,image registration,image reconstruction | Iterative reconstruction,Computer vision,Computer science,Iterative method,Algorithm,Artificial intelligence,Sampling (statistics),Mesh generation,Image registration,Iterative closest point,Image sampling | Conference |
Citations | PageRank | References |
73 | 7.17 | 13 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Natasha Gelfand | 1 | 1236 | 67.99 |
| Szymon Rusinkiewicz | 2 | 7029 | 350.57 |
| Leslie Ikemoto | 3 | 842 | 33.73 |
| Marc Levoy | 4 | 10273 | 1073.33 |