ReFiNe: Recursive Field Networks for Cross-modal Multi-scene Representation
<|reference_start|>ReFiNe: Recursive Field Networks for Cross-modal Multi-scene Representation: The common trade-offs of state-of-the-art methods for multi-shape representation (a single model "packing" multiple objects) involve trading modeling accuracy against memory and storage. We show how to encode multiple shapes represented as continuous neural fields with a higher degree of precision than previously possible and with low memory usage. Key to our approach is a recursive hierarchical formulation that exploits object self-similarity, leading to a highly compressed and efficient shape latent space. Thanks to the recursive formulation, our method supports spatial and global-to-local latent feature fusion without needing to initialize and maintain auxiliary data structures, while still allowing for continuous field queries to enable applications such as raytracing. In experiments on a set of diverse datasets, we provide compelling qualitative results and demonstrate state-of-the-art multi-scene reconstruction and compression results with a single network per dataset.<|reference_end|>
@article{zakharov2024refine:,
    title={ReFiNe: Recursive Field Networks for Cross-modal Multi-scene
  Representation},
    author={Sergey Zakharov, Katherine Liu, Adrien Gaidon, Rares Ambrus},
    journal={arXiv preprint arXiv:2406.04309},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.04309},
    primaryClass={cs.CV cs.GR cs.LG cs.MM}
}