Stereo-Matching Knowledge Distilled Monocular Depth Estimation Filtered by Multiple Disparity Consistency

• URL: http://arxiv.org/abs/2401.12019v2
• Date: Tue, 23 Jan 2024 03:16:43 GMT
• Title: Stereo-Matching Knowledge Distilled Monocular Depth Estimation Filtered by Multiple Disparity Consistency
• Authors: Woonghyun Ka, Jae Young Lee, Jaehyun Choi, Junmo Kim
• Abstract summary: We propose a method to identify and filter errors in the pseudo-depth map using multiple disparity maps. Experimental results show that the proposed method outperforms the previous methods.
• Score: 31.261772846687297
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In stereo-matching knowledge distillation methods of the self-supervised monocular depth estimation, the stereo-matching network's knowledge is distilled into a monocular depth network through pseudo-depth maps. In these methods, the learning-based stereo-confidence network is generally utilized to identify errors in the pseudo-depth maps to prevent transferring the errors. However, the learning-based stereo-confidence networks should be trained with ground truth (GT), which is not feasible in a self-supervised setting. In this paper, we propose a method to identify and filter errors in the pseudo-depth map using multiple disparity maps by checking their consistency without the need for GT and a training process. Experimental results show that the proposed method outperforms the previous methods and works well on various configurations by filtering out erroneous areas where the stereo-matching is vulnerable, especially such as textureless regions, occlusion boundaries, and reflective surfaces.

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