NerfAcc: Efficient Sampling Accelerates NeRFs
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.
645b166de276cc926eb4c59f
Ruilong Li
extracted_pending
2024-01-22T06:45:14.074Z
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/avatars/976066e1c1823345f3adc98e231569ae.svg
Ruilong Li
user
ruilongli
645b166de276cc926eb4c5a0
Hang Gao
extracted_pending
2023-07-06T13:18:28.218Z
64a6bec6b8689f49e8a8ea02
/avatars/870e4b534668dd38db70ed3811444b50.svg
Hang
user
hangg7
645b166de276cc926eb4c5a1
Matthew Tancik
645b166de276cc926eb4c5a2
Angjoo Kanazawa
extracted_confirmed
2023-06-01T17:04:53.441Z
6478cf7150ff7001631679c3
/avatars/65ec385a9cc44c972e6caf952e759ff1.svg
Angjoo Kanazawa
user
akanazawa
NerfAcc: Efficient Sampling Accelerates NeRFs
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.
NerfAcc accelerates Neural Radiance Field training and rendering by providing advanced, flexible sampling methods that reduce training time significantly.
Neural Radiance Fields volume rendering sampling approaches transmittance estimator NerfAcc PyTorch Instant-NGP