| Full text | |
| Author(s): |
Mendoza, Julio
;
Pedrini, Helio
;
Farinella, GM
;
Radeva, P
;
Braz, J
Total Authors: 5
|
| Document type: | Journal article |
| Source: | VISAPP: PROCEEDINGS OF THE 15TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS, VOL 4: VISAPP; v. N/A, p. 8-pg., 2020-01-01. |
| Abstract | |
In this work, we propose a self-supervised approach to depth estimation. Our method uses depth consistency to generate soft visibility mask that reduces the error contribution of inconsistent regions produced by occlusions. In addition, we allow the pose network to take advantage of the depth network representations to produce more accurate results. The experiments are conducted on the KITTI 2015 dataset. We analyze the effect of each component in the performance of the model and demonstrate that the consistency constraint and feature sharing can effectively improve our results. We show that our method is competitive when compared to the state of the art. (AU) | |
| FAPESP's process: | 17/12646-3 - Déjà vu: feature-space-time coherence from heterogeneous data for media integrity analytics and interpretation of events |
| Grantee: | Anderson de Rezende Rocha |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 18/00031-7 - Studies on genome comparison |
| Grantee: | João Meidanis |
| Support Opportunities: | Regular Research Grants |