RA-Depth: Resolution Adaptive Self-Supervised Monocular Depth Estimation

• URL: http://arxiv.org/abs/2207.11984v2
• Date: Tue, 26 Jul 2022 08:48:03 GMT
• Title: RA-Depth: Resolution Adaptive Self-Supervised Monocular Depth Estimation
• Authors: Mu He, Le Hui, Yikai Bian, Jian Ren, Jin Xie, Jian Yang
• Abstract summary: We propose a resolution adaptive self-supervised monocular depth estimation method (RA-Depth) by learning the scale invariance of the scene depth. RA-Depth achieves state-of-the-art performance, and also exhibits a good ability of resolution adaptation.
• Score: 27.679479140943503
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing self-supervised monocular depth estimation methods can get rid of expensive annotations and achieve promising results. However, these methods suffer from severe performance degradation when directly adopting a model trained on a fixed resolution to evaluate at other different resolutions. In this paper, we propose a resolution adaptive self-supervised monocular depth estimation method (RA-Depth) by learning the scale invariance of the scene depth. Specifically, we propose a simple yet efficient data augmentation method to generate images with arbitrary scales for the same scene. Then, we develop a dual high-resolution network that uses the multi-path encoder and decoder with dense interactions to aggregate multi-scale features for accurate depth inference. Finally, to explicitly learn the scale invariance of the scene depth, we formulate a cross-scale depth consistency loss on depth predictions with different scales. Extensive experiments on the KITTI, Make3D and NYU-V2 datasets demonstrate that RA-Depth not only achieves state-of-the-art performance, but also exhibits a good ability of resolution adaptation.

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