title: NerfAcc: Efficient Sampling Accelerates NeRFs
summary: Optimizing and rendering Neural Radiance Fields is computationally expensive
  due to the vast number of samples required by volume rendering. Recent works
  have included alternative sampling approaches to help accelerate their methods,
  however, they are often not the focus of the work. In this paper, we
  investigate and compare multiple sampling approaches and demonstrate that
  improved sampling is generally applicable across NeRF variants under an unified
  concept of transmittance estimator. To facilitate future experiments, we
  develop NerfAcc, a Python toolbox that provides flexible APIs for incorporating
  advanced sampling methods into NeRF related methods. We demonstrate its
  flexibility by showing that it can reduce the training time of several recent
  NeRF methods by 1.5x to 20x with minimal modifications to the existing
  codebase. Additionally, highly customized NeRFs, such as Instant-NGP, can be
  implemented in native PyTorch using NerfAcc.
paper_authors._id: 645b166de276cc926eb4c59f
paper_authors.name: Ruilong Li
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2024-01-22T06:45:14.074Z
paper_authors._id: 65ae0e61c14c3cf579024e73
paper_authors.avatarUrl: /avatars/976066e1c1823345f3adc98e231569ae.svg
paper_authors.fullname: Ruilong Li
paper_authors.type: user
paper_authors.user: ruilongli
paper_authors._id: 645b166de276cc926eb4c5a0
paper_authors.name: Hang Gao
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2023-07-06T13:18:28.218Z
paper_authors._id: 64a6bec6b8689f49e8a8ea02
paper_authors.avatarUrl: /avatars/870e4b534668dd38db70ed3811444b50.svg
paper_authors.fullname: Hang
paper_authors.type: user
paper_authors.user: hangg7
paper_authors._id: 645b166de276cc926eb4c5a1
paper_authors.name: Matthew Tancik
paper_authors._id: 645b166de276cc926eb4c5a2
paper_authors.name: Angjoo Kanazawa
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2023-06-01T17:04:53.441Z
paper_authors._id: 6478cf7150ff7001631679c3
paper_authors.avatarUrl: /avatars/65ec385a9cc44c972e6caf952e759ff1.svg
paper_authors.fullname: Angjoo Kanazawa
paper_authors.type: user
paper_authors.user: akanazawa
paper_title: NerfAcc: Efficient Sampling Accelerates NeRFs
paper_summary: Optimizing and rendering Neural Radiance Fields is computationally expensive
  due to the vast number of samples required by volume rendering. Recent works
  have included alternative sampling approaches to help accelerate their methods,
  however, they are often not the focus of the work. In this paper, we
  investigate and compare multiple sampling approaches and demonstrate that
  improved sampling is generally applicable across NeRF variants under an unified
  concept of transmittance estimator. To facilitate future experiments, we
  develop NerfAcc, a Python toolbox that provides flexible APIs for incorporating
  advanced sampling methods into NeRF related methods. We demonstrate its
  flexibility by showing that it can reduce the training time of several recent
  NeRF methods by 1.5x to 20x with minimal modifications to the existing
  codebase. Additionally, highly customized NeRFs, such as Instant-NGP, can be
  implemented in native PyTorch using NerfAcc.
paper_ai_keywords: Neural Radiance Fields (NeRF) volume rendering transmittance estimator NerfAcc PyTorch