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 this https URL

本研究提出了一种基于多视角视觉系统的方法， 使用低成本单目 RGB 相机获得准确的深度估计， 通过多张水平或垂直对齐的图像并利用新的启发式方法和鲁棒学习方法来融合多个代价体之间的数据以提高深度估计的性能，实验结果表明该方法优于传统的二帧立体匹配方法。