Structure-Guided Ranking Loss for Single Image Depth Prediction

Ke Xian, Jianming Zhang, Oliver Wang, Long Mai, Zhe Lin, Zhiguo Cao; The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 611-620

Abstract


Single image depth prediction is a challenging task due to its ill-posed nature and challenges with capturing ground truth for supervision. Large-scale disparity data generated from stereo photos and 3D videos is a promising source of supervision, however, such disparity data can only approximate the inverse ground truth depth up to an affine transformation. To more effectively learn from such pseudo-depth data, we propose to use a simple pair-wise ranking loss with a novel sampling strategy. Instead of randomly sampling point pairs, we guide the sampling to better characterize structure of important regions based on the low-level edge maps and high-level object instance masks. We show that the pair-wise ranking loss, combined with our structure-guided sampling strategies, can significantly improve the quality of depth map prediction. In addition, we introduce a new relative depth dataset of about 21K diverse high-resolution web stereo photos to enhance the generalization ability of our model. In experiments, we conduct cross-dataset evaluation on six benchmark datasets and show that our method consistently improves over the baselines, leading to superior quantitative and qualitative results.

Related Material


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[bibtex]
@InProceedings{Xian_2020_CVPR,
author = {Xian, Ke and Zhang, Jianming and Wang, Oliver and Mai, Long and Lin, Zhe and Cao, Zhiguo},
title = {Structure-Guided Ranking Loss for Single Image Depth Prediction},
booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}