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 language | English |
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Title of host publication | Proceedings : 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018 |
Publisher | IEEE |
Pages | 283-291 |
Number of pages | 9 |
ISBN (Electronic) | 9781538664209 |
DOIs | |
Publication status | Published - 2018 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2018 IEEE.