Name
Abstract | ||
|---|---|---|
•We formulate a new LRF with the projection by the scatter matrixs Eigen vectors and the mesh normal.•We firstly discuss the support radius for constructing the LRF and generating the feature descriptor separately.•In the experiments about Recall vs. 1-Precision Curve, we provide more precise measurements for TP (true positive) and FP (false positive).•Our methods concentrate on decreasing the calculation time and storage through all the processes. (e.g. low feature generation time and low feature dimension). |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.cag.2018.09.012 | Computers & Graphics |
Keywords | Field | DocType |
Local surface patch,Local reference frame,Local feature descriptor,Geometrical distribution,Feature matching | Computer vision,Feature fusion,Computer science,Robustness (computer science),Feature matching,Local reference frame,Artificial intelligence,Statistics,Scatter matrix,Eigenvalues and eigenvectors,Salient | Journal |
Volume | ISSN | Citations |
77 | 0097-8493 | 1 |
PageRank | References | Authors |
0.36 | 38 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Zhou | 1 | 15 | 2.92 |
| Caiwen Ma | 2 | 5 | 1.08 |
| Shenghui Liao | 3 | 70 | 14.44 |
| Jinjing Shi | 4 | 17 | 4.26 |
| Tong Yao | 5 | 7 | 2.12 |
| Peng Chang | 6 | 7 | 1.25 |
| Arjan Kuijper | 7 | 1063 | 133.22 |