MFuseNet: Robust Depth Estimation with Learned Multiscopic Fusion

• URL: http://arxiv.org/abs/2108.02448v2
• Date: Fri, 6 Aug 2021 07:31:12 GMT
• Title: MFuseNet: Robust Depth Estimation with Learned Multiscopic Fusion
• Authors: Weihao Yuan, Rui Fan, Michael Yu Wang, Qifeng Chen
• Abstract summary: We design a multiscopic vision system that utilizes a low-cost monocular RGB camera to acquire accurate depth estimation. Unlike multi-view stereo with images captured at unconstrained camera poses, the proposed system controls the motion of a camera to capture a sequence of images.
• Score: 47.2251122861135
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We design a multiscopic vision system that utilizes a low-cost monocular RGB camera to acquire accurate depth estimation. Unlike multi-view stereo with images captured at unconstrained camera poses, the proposed system controls the motion of a camera to capture a sequence of images in horizontally or vertically aligned positions with the same parallax. In this system, we propose a new heuristic method and a robust learning-based method to fuse multiple cost volumes between the reference image and its surrounding images. To obtain training data, we build a synthetic dataset with multiscopic images. The experiments on the real-world Middlebury dataset and real robot demonstration show that our multiscopic vision system outperforms traditional two-frame stereo matching methods in depth estimation. Our code and dataset are available at https://sites.google.com/view/multiscopic.

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