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Creators/Authors contains: "Xu, Danfei"

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  1. ; ; ; ; ; ; ; (, International Conference on Learning Representations 2025)
    Free, publicly-accessible full text available April 28, 2026
  2. ; ; (, Proceedings of The 8th Conference on Robot Learning)
    Free, publicly-accessible full text available November 8, 2025
  3. ; ; ; (, Proceedings of The 8th Conference on Robot Learning)
    Free, publicly-accessible full text available November 9, 2025
  4. ; ; ; ; ; ; (, IEEE/ASME Transactions on Mechatronics)
    Full Text Available 
  5. ; ; ; ; ; (, IEEE)
    This paper presents Neural Visibility Field (NVF), a novel uncertainty quantification method for Neural Radiance Fields (NeRF) applied to active mapping. Our key insight is that regions not visible in the training views lead to inherently unreliable color predictions by NeRF at this region, resulting in increased uncertainty in the synthesized views. To address this, we propose to use Bayesian Networks to composite position-based field uncertainty into ray-based uncertainty in camera observations. Consequently, NVF naturally assigns higher uncertainty to unobserved regions, aiding robots to select the most informative next viewpoints. Extensive evaluations show that NVF excels not only in uncertainty quantification but also in scene reconstruction for active mapping, outperforming existing methods. 
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