Camera Relocalization in Shadow-free Neural Radiance Fields
<|reference_start|>Camera Relocalization in Shadow-free Neural Radiance Fields: Camera relocalization is a crucial problem in computer vision and robotics. Recent advancements in neural radiance fields (NeRFs) have shown promise in synthesizing photo-realistic images. Several works have utilized NeRFs for refining camera poses, but they do not account for lighting changes that can affect scene appearance and shadow regions, causing a degraded pose optimization process. In this paper, we propose a two-staged pipeline that normalizes images with varying lighting and shadow conditions to improve camera relocalization. We implement our scene representation upon a hash-encoded NeRF which significantly boosts up the pose optimization process. To account for the noisy image gradient computing problem in grid-based NeRFs, we further propose a re-devised truncated dynamic low-pass filter (TDLF) and a numerical gradient averaging technique to smoothen the process. Experimental results on several datasets with varying lighting conditions demonstrate that our method achieves state-of-the-art results in camera relocalization under varying lighting conditions. Code and data will be made publicly available.<|reference_end|>
@article{xu2024camera,
    title={Camera Relocalization in Shadow-free Neural Radiance Fields},
    author={Shiyao Xu, Caiyun Liu, Yuantao Chen, Zhenxin Zhu, Zike Yan, Yongliang
  Shi, Hao Zhao, Guyue Zhou},
    journal={arXiv preprint arXiv:2405.14824},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.14824},
    primaryClass={cs.CV cs.RO}
}