Name
Abstract | ||
|---|---|---|
6D object pose estimation is widely applied in robotic tasks such as grasping and manipulation. Prior methods using RGB-only images are vulnerable to heavy occlusion and poor illumination, so it is important to complement them with depth information. However, existing methods using RGB-D data cannot adequately exploit consistent and complementary information between RGB and depth modalities. In this paper, we present a novel method to effectively consider the correlation within and across both modalities with attention mechanism to learn discriminative and compact multi-modal features. Then, effective fusion strategies for intra- and inter-correlation modules are explored to ensure efficient information flow between RGB and depth. To our best knowledge, this is the first work to explore effective intra- and inter-modality fusion in 6D pose estimation. The experimental results show that our method can achieve the state-of-the-art performance on LineMOD and YCB-Video dataset. We also demonstrate that the proposed method can benefit a real-world robot grasping task by providing accurate object pose estimation. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICPR48806.2021.9412238 | 2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR) |
Keywords | DocType | ISSN |
object pose estimation, RGB-D, correlation fusion | Conference | 1051-4651 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yi Cheng | 1 | 5 | 1.44 |
| Hongyuan Zhu | 2 | 109 | 16.59 |
| Ying Sun | 3 | 0 | 0.34 |
| Cihan Acar | 4 | 0 | 0.34 |
| Wei Jing | 5 | 32 | 13.31 |
| Yan Wu | 6 | 60 | 11.16 |
| Liyuan Li | 7 | 912 | 61.31 |
| Cheston Tan | 8 | 155 | 15.27 |
| Joo-Hwee Lim | 9 | 0 | 2.70 |