GeoNet: Geometric Neural Network for Joint Depth and Surface Normal Estimation

Xiaojuan QI, Renjie LIAO, Zhengzhe LIU, Raquel URTASUN, Jiaya JIA

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

300 Citations (Scopus)

Abstract

In this paper, we propose Geometric Neural Network (GeoNet) to jointly predict depth and surface normal maps from a single image. Building on top of two-stream CNNs, our GeoNet incorporates geometric relation between depth and surface normal via the new depth-to-normal and normal-to-depth networks. Depth-to-normal network exploits the least square solution of surface normal from depth and improves its quality with a residual module. Normal-to-depth network, contrarily, refines the depth map based on the constraints from the surface normal through a kernel regression module, which has no parameter to learn. These two networks enforce the underlying model to efficiently predict depth and surface normal for high consistency and corresponding accuracy. Our experiments on NYU v2 dataset verify that our GeoNet is able to predict geometrically consistent depth and normal maps. It achieves top performance on surface normal estimation and is on par with state-of-the-art depth estimation methods.
Original languageEnglish
Title of host publicationProceedings : 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018
PublisherIEEE
Pages283-291
Number of pages9
ISBN (Electronic)9781538664209
DOIs
Publication statusPublished - 2018
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

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