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#Stereo Matching (2)

WaterfallPagination
StereoPolicy: Improving Robotic Manipulation Policies via Stereo Perception

StereoPolicy: Improving Robotic Manipulation Policies via Stereo Perception

StereoPolicy learns task-conditioned binocular representations directly from synchronized stereo pairs instead of reconstructing explicit depth or point clouds. A Stereo Transformer alternates within-image and cross-image attention over shared 2D features, and the fused representation integrates with diffusion policies, pi0.5, and GR00T-N1.5. Experiments across simulation and real tabletop and bimanual mobile manipulation show consistent gains over RGB, RGB-D, point-cloud, and multi-view baselines, together with practical guidance on stereo baseline and working distance.

Evans Han, Yunfan Jiang, Yingke Wang · 50 views · May 11, 2026
Stereo visionRobot Manipulation
May 11, 2026
Fast-FoundationStereo: Real-Time Zero-Shot Stereo Matching

Fast-FoundationStereo: Real-Time Zero-Shot Stereo Matching

Fast-FoundationStereo accelerates the three bottlenecks of FoundationStereo separately: distillation compresses the hybrid monocular+stereo backbone into one efficient student, blockwise NAS searches cost-filtering designs under latency budgets with ILP, and structured pruning guided by a recurrent dependency graph slims the ConvGRU refinement module. Trained with 1.4M auto pseudo-labeled in-the-wild stereo pairs, it runs over 10x faster than FoundationStereo while nearly matching zero-shot accuracy, setting a new state of the art among real-time methods on Middlebury, ETH3D, KITTI and Booster.

Bowen Wen, Shaurya Dewan, Stan Birchfield · 662 views · Dec 11, 2025
Stereo MatchingDepth Estimation
Dec 11, 2025

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