Name
Abstract | ||
|---|---|---|
3D shape completion is important to enable machines to perceive the complete geometry of objects from partial observations. To address this problem, view-based methods have been presented. These methods represent shapes as multiple depth images, which can be back-projected to yield corresponding 3D point clouds, and they perform shape completion by learning to complete each depth image using neural networks. While view-based methods lead to state-of-the-art results, they currently do not enforce geometric consistency among the completed views during the inference stage. To resolve this issue, we propose a multi-view consistent inference technique for 3D shape completion, which we express as an energy minimization problem including a data term and a regularization term. We formulate the regularization term as a consistency loss that encourages geometric consistency among multiple views, while the data term guarantees that the optimized views do not drift away too much from a learned shape descriptor. Experimental results demonstrate that our method completes shapes more accurately than previous techniques. |
Year | Venue | DocType |
|---|---|---|
2020 | AAAI | Conference |
Volume | ISSN | Citations |
34 | 2159-5399 | 3 |
PageRank | References | Authors |
0.37 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tao Hu | 1 | 3 | 0.71 |
| Han Zhizhong | 2 | 198 | 18.28 |
| Zwicker Matthias | 3 | 2513 | 129.25 |