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Creators/Authors contains: "Tang, Hao"

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  1. ; ; ; ; ; ; ; ; (, IEEE International Conference on Intelligent Robots and Systems)
    To address the challenge of short-term object pose tracking in dynamic environments with monocular RGB input, we introduce a large-scale synthetic dataset Omni-Pose6D, crafted to mirror the diversity of real-world conditions. We additionally present a benchmarking framework for a comprehensive comparison of pose tracking algorithms. We propose a pipeline featuring an uncertainty-aware keypoint refinement network, employing probabilistic modeling to refine pose estimation. Comparative evaluations demonstrate that our approach achieves performance superior to existing baselines on real datasets, underscoring the effectiveness of our synthetic dataset and refinement technique in enhancing tracking precision in dynamic contexts. Our contributions set a new precedent for the development and assessment of object pose tracking methodologies in complex scenes. 
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    Free, publicly-accessible full text available October 25, 2026
  2. ; ; (, Journal of Chemical Information and Modeling)
    Free, publicly-accessible full text available July 14, 2026
  3. ; ; ; ; ; (, Journal of Applied Remote Sensing)
    Free, publicly-accessible full text available January 1, 2026
  4. ; ; (, ACM)
    Full Text Available 
  5. ; ; ; (, IEEE)
    Free, publicly-accessible full text available February 26, 2026
  6. ; ; (, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR))
    Accepted Manuscript Full Text Available
  7. ; ; ; (, ACM)
    Full Text Available 
  8. ; ; ; ; (, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR))
    Accepted Manuscript Full Text Available
  9. ; ; ; ; (, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR))
    Accepted Manuscript Full Text Available
  10. ; ; ; ; (, The 18th IEEE International Conference on Automatic Face and Gesture Recognition)
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