Name
Abstract | ||
|---|---|---|
Accurate estimation of 3D object pose is highly desirable in a wide range of applications, such as robotics and augmented reality. Although significant advancement has been made for pose estimation, there is room for further improvement. Recent pose estimation systems utilize an iterative refinement process to revise the predicted pose to obtain a better final output. However, such refinement process only takes account of geometric features for pose revision during the iteration. Motivated by this approach, this paper designs a novel iterative refinement process that deals with both color and geometric features for object pose refinement. Experiments show that the proposed method is able to reach 94.74% and 93.2% in ADD(-S) metric with only 2 iterations, outperforming the state-of-the-art methods on the LINEMOD and YCB-Video datasets, respectively. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.3390/s20154114 | SENSORS |
Keywords | DocType | Volume |
object pose estimation,LINEMOD,deep learning,convolution neural network | Journal | 20 |
Issue | ISSN | Citations |
15 | 1424-8220 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shao-Kang Huang | 1 | 3 | 1.76 |
| Chen-Chien James Hsu | 2 | 38 | 11.17 |
| Wei-Yen Wang | 3 | 995 | 87.40 |
| Cheng-Hung Lin | 4 | 263 | 25.30 |