Name
Abstract | ||
|---|---|---|
This paper proposes a field model for repairing 3D shapes constructed from multi-view RGB data. Specifically, we represent a 3D shape in a Markov random field (MRF) in which the geometric information is encoded by random binary variables and the appearance information is retrieved from a set of RGB images captured at multiple viewpoints. The local priors in the MRF model capture the local structures of object shapes and are learnt from 3D shape templates using a convolutional deep belief network. Repairing a 3D shape is formulated as the maximum a posteriori (MAP) estimation in the corresponding MRF. Variational mean field approximation technique is adopted for the MAP estimation. The proposed method was evaluated on both artificial data and real data obtained from reconstruction of practical scenes. Experimental results have shown the robustness and efficiency of the proposed method in repairing noisy and incomplete 3D shapes. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/CVPR.2016.612 | 2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) |
Field | DocType | Volume |
Computer vision,Pattern recognition,Markov random field,Computer science,Deep belief network,Robustness (computer science),Mean field theory,RGB color model,Artificial intelligence,Maximum a posteriori estimation,Prior probability,Binary number | Conference | 2016 |
Issue | ISSN | Citations |
1 | 1063-6919 | 4 |
PageRank | References | Authors |
0.41 | 17 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Duc Thanh Nguyen | 1 | 262 | 23.73 |
| Binh-Son Hua | 2 | 99 | 12.08 |
| Minh-Khoi Tran | 3 | 18 | 2.01 |
| Quang-Hieu Pham | 4 | 32 | 2.47 |
| Sai Kit Yeung | 5 | 60 | 4.97 |