Lightplane: Highly-Scalable Components for Neural 3D Fields
Authors: name: Ang Cao, name: Justin Johnson, name: Andrea Vedaldi, name: David Novotny

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:
https://github.com/facebookresearch/lightplane.