Name
Abstract | ||
|---|---|---|
Robots and other smart devices need efficient object-based scene representations from their on-board vision systems to reason about contact, physics and occlusion. Recognized precise object models will play an important role alongside non-parametric reconstructions of unrecognized structures. We present a system which can estimate the accurate poses of multiple known objects in contact and occlusion from real-time, embodied multi-view vision. Our approach makes 3D object pose proposals from single RGB-D views, accumulates pose estimates and non-parametric occupancy information from multiple views as the camera moves, and performs joint optimization to estimate consistent, non-intersecting poses for multiple objects in contact. We verify the accuracy and robustness of our approach experimentally on 2 object datasets: YCB-Video, and our own challenging Cluttered YCB-Video. We demonstrate a real-time robotics application where a robot arm precisely and orderly disassembles complicated piles of objects, using only on-board RGB-D vision. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/CVPR42600.2020.01455 | CVPR |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
22 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kentaro Wada | 1 | 1 | 3.43 |
| Edgar Sucar | 2 | 2 | 0.70 |
| Stephen James | 3 | 58 | 6.02 |
| Lenton Daniel | 4 | 0 | 0.34 |
| Andrew J. Davison | 5 | 6707 | 350.85 |