Digging Into Self-Supervised Monocular Depth Estimation

Per-pixel ground-truth depth data is challenging to acquire at scale. To overcome this limitation, self-supervised learning has emerged as a promising alternative for training models to perform monocular depth estimation... In this paper, we propose a set of improvements, which together result in both quantitatively and qualitatively improved depth maps compared to competing self-supervised methods. Research on self-supervised monocular training usually explores increasingly complex architectures, loss functions, and image formation models, all of which have recently helped to close the gap with fully-supervised methods. We show that a surprisingly simple model, and associated design choices, lead to superior predictions. In particular, we propose (i) a minimum reprojection loss, designed to robustly handle occlusions, (ii) a full-resolution multi-scale sampling method that reduces visual artifacts, and (iii) an auto-masking loss to ignore training pixels that violate camera motion assumptions. We demonstrate the effectiveness of each component in isolation, and show high quality, state-of-the-art results on the KITTI benchmark. read more

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Datasets


Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Monocular Depth Estimation KITTI Eigen split unsupervised Monodepth2 M absolute relative error 0.115 # 16
Monocular Depth Estimation KITTI Eigen split unsupervised Monodepth2 S absolute relative error 0.109 # 12
Monocular Depth Estimation KITTI Eigen split unsupervised Monodepth2 MS absolute relative error 0.106 # 10

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