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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.