Lightplane: Highly-Scalable Components for Neural 3D Fields
<|reference_start|>Lightplane: Highly-Scalable Components for Neural 3D Fields: Contemporary 3D research, particularly in reconstruction and generation, heavily relies on 2D images for inputs or supervision. However, current designs for these 2D-3D mapping are memory-intensive, posing a significant bottleneck for existing methods and hindering new applications. In response, we propose a pair of highly scalable components for 3D neural fields: Lightplane Render and Splatter, which significantly reduce memory usage in 2D-3D mapping. These innovations enable the processing of vastly more and higher resolution images with small memory and computational costs. We demonstrate their utility in various applications, from benefiting single-scene optimization with image-level losses to realizing a versatile pipeline for dramatically scaling 3D reconstruction and generation. Code: \url{https://github.com/facebookresearch/lightplane}.<|reference_end|>
@article{cao2024lightplane:,
    title={Lightplane: Highly-Scalable Components for Neural 3D Fields},
    author={Ang Cao, Justin Johnson, Andrea Vedaldi, David Novotny},
    journal={arXiv preprint arXiv:2404.19760},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.19760},
    primaryClass={cs.CV cs.GR}
}