Bifuse: Monocular 360 depth estimation via bi-projection fusion

Fu En Wang, Yu Hsuan Yeh, Min Sun, Wei-Chen Chiu, Yi Hsuan Tsai

研究成果: Conference article同行評審

3 引文 斯高帕斯(Scopus)

摘要

Depth estimation from a monocular 360 image is an emerging problem that gains popularity due to the availability of consumer-level 360 cameras and the complete surrounding sensing capability. While the standard of 360 imaging is under rapid development, we propose to predict the depth map of a monocular 360 image by mimicking both peripheral and foveal vision of the human eye. To this end, we adopt a two-branch neural network leveraging two common projections: equirectangular and cubemap projections. In particular, equirectangular projection incorporates a complete field-of-view but introduces distortion, whereas cubemap projection avoids distortion but introduces discontinuity at the boundary of the cube. Thus we propose a bi-projection fusion scheme along with learnable masks to balance the feature map from the two projections. Moreover, for the cubemap projection, we propose a spherical padding procedure which mitigates discontinuity at the boundary of each face. We apply our method to four panorama datasets and show favorable results against the existing state-of-the-art methods.

原文English
文章編號9157424
頁(從 - 到)459-468
頁數10
期刊Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
DOIs
出版狀態Published - 2020
事件2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020 - Virtual, Online, United States
持續時間: 14 六月 202019 六月 2020

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