Neural NeRF Compression
<|reference_start|>Neural NeRF Compression: Neural Radiance Fields (NeRFs) have emerged as powerful tools for capturing detailed 3D scenes through continuous volumetric representations. Recent NeRFs utilize feature grids to improve rendering quality and speed; however, these representations introduce significant storage overhead. This paper presents a novel method for efficiently compressing a grid-based NeRF model, addressing the storage overhead concern. Our approach is based on the non-linear transform coding paradigm, employing neural compression for compressing the model's feature grids. Due to the lack of training data involving many i.i.d scenes, we design an encoder-free, end-to-end optimized approach for individual scenes, using lightweight decoders. To leverage the spatial inhomogeneity of the latent feature grids, we introduce an importance-weighted rate-distortion objective and a sparse entropy model employing a masking mechanism. Our experimental results validate that our proposed method surpasses existing works in terms of grid-based NeRF compression efficacy and reconstruction quality.<|reference_end|>
@article{pham2024neural,
    title={Neural NeRF Compression},
    author={Tuan Pham, Stephan Mandt},
    journal={arXiv preprint arXiv:2406.08943},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.08943},
    primaryClass={cs.CV cs.LG}
}