Name
Abstract | ||
|---|---|---|
Recent advances in deep learning have shown high success in obtaining the 6-DoF pose of rigid objects. However, most works rely on a pre-existing dataset and do not tackle the data gathering part. The time-consuming and tedious tasks required to build datasets are, to a large extent, what is keeping these techniques from being more widely used in practical applications. We present a whole pipeline from data gathering to pose recognition and an example application of robot grasping. For our data gathering method we require as minimum user intervention as possible and, even without using depth information or 3D models, by using a novel RGB-only Neural Network design we are able to obtain results very close to the state of the art. We call this method Affordable Pose Estimation (APE). |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICIP40778.2020.9190664 | 2020 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) |
Keywords | DocType | ISSN |
Pose Recognition, Dataset Generation, Robot Grasping | Conference | 1522-4880 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Antonio Gabas | 1 | 10 | 1.93 |
| Yoshiyasu, Y. | 2 | 16 | 5.64 |
| Rohan Pratap Singh | 3 | 0 | 1.01 |
| Ryusuke Sagawa | 4 | 631 | 52.61 |
| Eiichi Yoshida | 5 | 522 | 59.13 |