title: NeRF-XL: Scaling NeRFs with Multiple GPUs
summary: We present NeRF-XL, a principled method for distributing Neural Radiance
  Fields (NeRFs) across multiple GPUs, thus enabling the training and rendering
  of NeRFs with an arbitrarily large capacity. We begin by revisiting existing
  multi-GPU approaches, which decompose large scenes into multiple independently
  trained NeRFs, and identify several fundamental issues with these methods that
  hinder improvements in reconstruction quality as additional computational
  resources (GPUs) are used in training. NeRF-XL remedies these issues and
  enables the training and rendering of NeRFs with an arbitrary number of
  parameters by simply using more hardware. At the core of our method lies a
  novel distributed training and rendering formulation, which is mathematically
  equivalent to the classic single-GPU case and minimizes communication between
  GPUs. By unlocking NeRFs with arbitrarily large parameter counts, our approach
  is the first to reveal multi-GPU scaling laws for NeRFs, showing improvements
  in reconstruction quality with larger parameter counts and speed improvements
  with more GPUs. We demonstrate the effectiveness of NeRF-XL on a wide variety
  of datasets, including the largest open-source dataset to date, MatrixCity,
  containing 258K images covering a 25km^2 city area.
paper_authors._id: 662b29690ee2fbf66814a3fb
paper_authors.name: Ruilong Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-04-26T09:43:52.539Z
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paper_authors.avatarUrl: /avatars/976066e1c1823345f3adc98e231569ae.svg
paper_authors.fullname: Ruilong Li
paper_authors.type: user
paper_authors.user: ruilongli
paper_authors._id: 662b29690ee2fbf66814a3fc
paper_authors.name: Sanja Fidler
paper_authors._id: 662b29690ee2fbf66814a3fd
paper_authors.name: Angjoo Kanazawa
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-04-26T09:44:06.153Z
paper_authors._id: 6478cf7150ff7001631679c3
paper_authors.avatarUrl: /avatars/65ec385a9cc44c972e6caf952e759ff1.svg
paper_authors.fullname: Angjoo Kanazawa
paper_authors.type: user
paper_authors.user: akanazawa
paper_authors._id: 662b29690ee2fbf66814a3fe
paper_authors.name: Francis Williams
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-04-26T09:44:27.570Z
paper_authors._id: 6581e26005c177eea396fb6e
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/kM0Gm4e3tXmE1WyFLNcaU.jpeg
paper_authors.fullname: Francis Williams
paper_authors.type: user
paper_authors.user: fwilliams
paper_title: NeRF-XL: Scaling NeRFs with Multiple GPUs
paper_summary: We present NeRF-XL, a principled method for distributing Neural Radiance
  Fields (NeRFs) across multiple GPUs, thus enabling the training and rendering
  of NeRFs with an arbitrarily large capacity. We begin by revisiting existing
  multi-GPU approaches, which decompose large scenes into multiple independently
  trained NeRFs, and identify several fundamental issues with these methods that
  hinder improvements in reconstruction quality as additional computational
  resources (GPUs) are used in training. NeRF-XL remedies these issues and
  enables the training and rendering of NeRFs with an arbitrary number of
  parameters by simply using more hardware. At the core of our method lies a
  novel distributed training and rendering formulation, which is mathematically
  equivalent to the classic single-GPU case and minimizes communication between
  GPUs. By unlocking NeRFs with arbitrarily large parameter counts, our approach
  is the first to reveal multi-GPU scaling laws for NeRFs, showing improvements
  in reconstruction quality with larger parameter counts and speed improvements
  with more GPUs. We demonstrate the effectiveness of NeRF-XL on a wide variety
  of datasets, including the largest open-source dataset to date, MatrixCity,
  containing 258K images covering a 25km^2 city area.
paper_ai_summary: NeRF-XL enables the scalable training and rendering of large-capacity NeRFs across multiple GPUs, demonstrating improved reconstruction quality and speed on extensive datasets.
paper_ai_keywords: Neural Radiance Fields (NeRFs) multi-GPU approaches distributed training rendering formulation parameter counts scaling laws MatrixCity