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Self-supervised Depth Estimation based on Feature Sharing and Consistency Constraints

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Autor(es):
Mendoza, Julio ; Pedrini, Helio ; Farinella, GM ; Radeva, P ; Braz, J
Número total de Autores: 5
Tipo de documento: Artigo Científico
Fonte: 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.
Resumo

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)

Processo FAPESP: 17/12646-3 - Déjà vu: coerência temporal, espacial e de caracterização de dados heterogêneos para análise e interpretação de integridade
Beneficiário:Anderson de Rezende Rocha
Modalidade de apoio: Auxílio à Pesquisa - Temático
Processo FAPESP: 18/00031-7 - Estudos sobre comparação de genomas
Beneficiário:João Meidanis
Modalidade de apoio: Auxílio à Pesquisa - Regular