JointRF: End-to-End Joint Optimization for Dynamic Neural Radiance Field Representation and Compression
<|reference_start|>JointRF: End-to-End Joint Optimization for Dynamic Neural Radiance Field Representation and Compression: Neural Radiance Field (NeRF) excels in photo-realistically static scenes, inspiring numerous efforts to facilitate volumetric videos. However, rendering dynamic and long-sequence radiance fields remains challenging due to the significant data required to represent volumetric videos. In this paper, we propose a novel end-to-end joint optimization scheme of dynamic NeRF representation and compression, called JointRF, thus achieving significantly improved quality and compression efficiency against the previous methods. Specifically, JointRF employs a compact residual feature grid and a coefficient feature grid to represent the dynamic NeRF. This representation handles large motions without compromising quality while concurrently diminishing temporal redundancy. We also introduce a sequential feature compression subnetwork to further reduce spatial-temporal redundancy. Finally, the representation and compression subnetworks are end-to-end trained combined within the JointRF. Extensive experiments demonstrate that JointRF can achieve superior compression performance across various datasets.<|reference_end|>
@article{zheng2024jointrf:,
    title={JointRF: End-to-End Joint Optimization for Dynamic Neural Radiance Field
  Representation and Compression},
    author={Zihan Zheng, Houqiang Zhong, Qiang Hu, Xiaoyun Zhang, Li Song, Ya
  Zhang, Yanfeng Wang},
    journal={arXiv preprint arXiv:2405.14452},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.14452},
    primaryClass={cs.CV cs.AI}
}