stereo-vision

EyeRobot 2.0 介绍了一种受人类视觉启发的机器人操作框架，通过双目相机上的主动注视（Active Visual Fixation）实现精细双臂操作，无需腕部相机。研究在真实与模拟任务中进行了上千次试验，证明该方法在物体遮挡腕部相机时可将成功率提升至对手的两倍以上。

This paper proposes distilling deep optical flow stereo methods into a single-satellite model for efficient retrieval of dense three-dimensional wind fields, improving accuracy over traditional atmospheric motion vectors in certain spectral bands.

Introduces StereoPolicy, a framework that leverages synchronized stereo image pairs to improve geometric reasoning for robot manipulation policies, avoiding the fragility of RGB-D and point clouds. It integrates with diffusion-based and vision-language-action policies, showing consistent improvements in simulation and real-world tasks.