Name
Abstract | ||
|---|---|---|
Estimating the 6D object pose is a very challenging task in computer vision. The main difficulty is mapping the object from RGB images to 3D space. In this paper, we present a novel two-stage method for estimating the 6D object pose by using the 2D keypoints of an object and its 2D bounding box. There are two stages in our method. The first stage detects the 2D keypoints and 2D bounding boxes of objects by a stable end-to-end framework. During the training phase, this framework uses viewpoint transformation information and object saliency regions to learn geometrically and semantically consistent keypoints. Then the 6D poses of objects are calculated by a series of geometric reasoning algorithms in the second stage. Experiments show that our method achieves accurate pose estimation and robust to occluded and cluttered scenes. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.neucom.2019.12.108 | Neurocomputing |
Keywords | DocType | Volume |
6D object pose estimation,Keypoints detection,Convolutional neural networks,Geometric reasoning | Journal | 389 |
ISSN | Citations | PageRank |
0925-2312 | 1 | 0.38 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wanqing Zhao | 1 | 15 | 7.07 |
| Shaobo Zhang | 2 | 18 | 7.03 |
| Ziyu Guan | 3 | 553 | 38.43 |
| Hangzai Luo | 4 | 2 | 1.41 |
| Lei Tang | 5 | 117 | 9.17 |
| Jinye Peng | 6 | 284 | 40.93 |
| Jianping Fan | 7 | 2677 | 192.33 |