Skip to main content

Research Repository

Advanced Search

Bi-projection-based Foreground-aware Omnidirectional Depth Prediction

Feng, Qi; Shum, Hubert P.H.; Morishima, Shigeo

Authors

Qi Feng

Shigeo Morishima



Abstract

Due to the increasing availability of commercial 360- degree cameras, accurate depth prediction for omnidirectional images can be beneficial to a wide range of applications including video editing and augmented reality. Regarding existing methods, some focus on learning high-quality global prediction while fail to capture detailed local features. Others suggest integrating local context into the learning procedure, they yet propose to train on non-foreground-aware databases. In this paper, we explore to simultaneously use equirectangular and cube-map projection to learn omnidirectional depth prediction from foreground-aware databases in a multi-task manner. Experimental results demonstrate improved performance when compared to the state-of-the-art.

Citation

Feng, Q., Shum, H. P., & Morishima, S. (2021). Bi-projection-based Foreground-aware Omnidirectional Depth Prediction.

Conference Name Visual Computing 2021
Conference Location Online
Start Date Sep 28, 2023
End Date Oct 1, 2021
Acceptance Date Aug 1, 2021
Publication Date 2021
Deposit Date Aug 13, 2021
Public URL https://durham-repository.worktribe.com/output/1138596
Publisher URL https://cgvi.jp/vc2021/program/oral/


You might also like



Downloadable Citations