title: Lightplane: Highly-Scalable Components for Neural 3D Fields
summary: 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.
paper_authors._id: 6631ca1a351231c4280963d1
paper_authors.name: Ang Cao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-05-02T08:37:10.576Z
paper_authors._id: 63a1c5dde36f2e4d5b0d9ca2
paper_authors.avatarUrl: /avatars/fd9adbada165f077427ef98af854a9f4.svg
paper_authors.fullname: Ang Cao
paper_authors.type: user
paper_authors.user: AACEE
paper_authors._id: 6631ca1a351231c4280963d2
paper_authors.name: Justin Johnson
paper_authors._id: 6631ca1a351231c4280963d3
paper_authors.name: Andrea Vedaldi
paper_authors._id: 6631ca1a351231c4280963d4
paper_authors.name: David Novotny
paper_title: Lightplane: Highly-Scalable Components for Neural 3D Fields
paper_summary: 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.
paper_ai_summary: Lightplane Render and Splatter components reduce memory usage in 2D-3D mapping for 3D neural fields, enabling efficient processing and scalable reconstruction and generation.
paper_ai_keywords: 3D neural fields Lightplane Render Splatter 2D-3D mapping memory usage