A unified dual-shutter framework and dual-stream network jointly correct blur and rolling-shutter distortion to reconstruct high-speed video from real-world degraded image pairs.
Stereo anything: Unifying stereo matching with large- scale mixed data
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
PicoEyes delivers a unified end-to-end model for full 3D gaze estimation including eye parameters, axes, segmentation and depth from monocular or binocular near-eye images, supported by a new large-scale multi-view dataset.
Lite Any Stereo delivers top-ranked zero-shot accuracy on four real-world stereo benchmarks using a lightweight backbone, hybrid cost aggregation, and three-stage training on million-scale data, at less than 1% of typical computational cost.
ROVR is a new diverse depth dataset for autonomous driving with 200K frames, released pipelines, and ablations showing sparse ground truth supports model training.
citing papers explorer
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Moment-Reenacting: Inverse Motion Degradation with Cross-shutter Guidance
A unified dual-shutter framework and dual-stream network jointly correct blur and rolling-shutter distortion to reconstruct high-speed video from real-world degraded image pairs.
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PicoEyes: Unified Gaze Estimation Framework for Mixed Reality with a Large-Scale Multi-View Dataset
PicoEyes delivers a unified end-to-end model for full 3D gaze estimation including eye parameters, axes, segmentation and depth from monocular or binocular near-eye images, supported by a new large-scale multi-view dataset.
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Lite Any Stereo: Efficient Zero-Shot Stereo Matching
Lite Any Stereo delivers top-ranked zero-shot accuracy on four real-world stereo benchmarks using a lightweight backbone, hybrid cost aggregation, and three-stage training on million-scale data, at less than 1% of typical computational cost.
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ROVR-Open-Dataset: A Large-Scale Depth Dataset for Autonomous Driving
ROVR is a new diverse depth dataset for autonomous driving with 200K frames, released pipelines, and ablations showing sparse ground truth supports model training.