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We design a multiscopic vision system that utilizes a low-cost monocular RGBcamera to acquire accurate depth estimation. Unlike multi-view stereo withimages captured at unconstrained camera poses, the proposed system controls themotion of a camera to capture a sequence of images in horizontally orvertically aligned positions with the same parallax. In this system, we proposea new heuristic method and a robust learning-based method to fuse multiple costvolumes between the reference image and its surrounding images. To obtaintraining data, we build a synthetic dataset with multiscopic images. Theexperiments on the real-world Middlebury dataset and real robot demonstrationshow that our multiscopic vision system outperforms traditional two-framestereo matching methods in depth estimation. Our code and dataset are availableat https://sites.google.com/view/multiscopic.

MFuseNet: Robust Depth Estimation With Learned Multiscopic Fusion