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NerfAcc: Efficient Sampling Accelerates NeRFs

Ruilong Li, Hang Gao, Matthew Tancik, Angjoo Kanazawa

arXiv:2305.04966v2cs.CV

TL;DR

NeRF optimization and rendering are computationally expensive, and efficient sampling has received limited attention despite being difficult to integrate across implementations. The paper unifies advanced sampling through transmittance estimation and provides NerfAcc, reporting 1.5× to 20× training speedup across several methods with better performance.

  • Problem

    NeRF optimization and rendering are computationally expensive, while efficient sampling has received limited attention and can require substantial implementation effort.

    Source: §1 · p. 1

  • Method

    The paper compares advanced sampling methods through a unified transmittance-estimator view and provides NerfAcc, a flexible Python API for integrating them into NeRF methods.

    Source: §1 · p. 2, §3 · p. 2

  • Results

    1.5× to 20× training speedup with better performance was reported across several NeRF methods after less than 100 lines of code change using NerfAcc.

    Source: §1 · p. 2

  • Takeaways & Limitations

    Combining an occupancy grid with a proposal network enables skipping empty space while concentrating samples onto surfaces.

    Source: §3.3 · p. 5

    Sampling options trade off efficiency and coverage: spatial skipping still samples occupied occluded areas, while PDF-based sampling spreads samples across space without skipping.

    Source: §3.3 · p. 5

Abstract

from arXiv · show

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.

1. Introduction

NeRF optimization and rendering are computationally expensive, while efficient sampling has received less attention and can be difficult to transfer between implementations. The paper unifies advanced sampling through transmittance estimation and introduces NerfAcc to integrate sampling methods across NeRF variants.

1. Introduction

  • NeRF optimization and rendering are costly because of the neural radiance-field representation and the large number of volume-rendering samples.The paper says these challenges have limited practical applications of NeRF-based optimization and rendering.Source: §1 · p. 1
  • Efficient sampling has received limited attention compared with efficient radiance-field representations, despite its use in some recent methods.Those sampling approaches are often not the focus of their papers.Source: §1 · pp. 1–2
  • Advanced sampling is difficult to adopt because implementations such as spatial skipping are tightly coupled to specific radiance-field codebases.Instant-NGP and Plenoxels use highly customized CUDA implementations, making these approaches challenging to reuse in other research.Source: §1 · p. 2
  • 1.5× to 20× training speedup with better performance was achieved across several NeRF methods with less than 100 lines of code change using NerfAcc.The paper also reports that a pure-Python Instant-NGP implementation matched the official CUDA implementation's training time and achieved +0.2dB performance.Source: §1 · p. 2
  • Advanced sampling approaches can be understood as estimating transmittance for importance sampling, providing a unified view across methods.The paper presents this unification with mathematical proofs and says it can support exploration of novel sampling strategies.Source: §1 · p. 2

2. Related Works

NeRF codebases and frameworks support different tasks and methods, but transferring implementations or using techniques outside a framework can require substantial effort. NerfAcc is presented as a standalone, plug-and-play sampling toolbox for existing codebases.

NeRF Codebases

  • NerfAcc is a plug-and-play toolbox intended to make sampling methods easy to integrate across NeRF tasks and existing codebases.The paper contrasts this with codebases tailored to particular tasks and methods, where transferring implementations requires non-trivial effort.Source: §2 · p. 2

NeRF Frameworks

  • NeRF-Factory, Nerfstudio, and Kaolin-Wisp consolidate variants, techniques, or voxel-based methods within shared frameworks.The paper describes these projects as facilitating future NeRF-related research.Source: §2 · p. 2
  • Unlike framework-centered projects, NerfAcc is designed as a standalone library for users working in their own codebases.The paper notes that frameworks may not benefit users outside them and that keeping a single framework current is difficult amid active NeRF research.Source: §2 · p. 2

3. Importance Sampling via Transmittance

The section unifies spatial-skipping and PDF-based sampling as ways to estimate transmittance along a ray, then use that estimate for importance sampling. It compares their formulations and design tradeoffs, including aliasing and opportunities to combine approaches.

3. Importance Sampling via Transmittance

  • Spatial skipping and PDF-based approaches both estimate transmittance along a ray, providing a shared basis for importance sampling.The paper presents this unified view as a framework for comparing sampling methods and exploring new strategies.Source: §3 · pp. 2–3, Figure 2 · p. 3

3.1. Formulation

  • Inverse sampling the CDF is equivalent to inverse sampling transmittance, so the estimator determines sample placement; the CDF can be computed as 1 − T(t).This concentrates samples where transmittance changes rapidly, such as near surfaces, without the computationally expensive integral.Source: §3.1 · pp. 3–4
  • Uniform sampling assumes equal contribution, spatial skipping omits empty regions, and PDF approaches use density estimates or proposal networks to distribute samples.Instant-NGP updates cached density with EMA, while the cited PDF approaches update their transmittance estimator through gradient descent.Source: §3.1 · p. 4

3.2. Design Spaces

  • Transmittance estimators can use voxel, MLP, or hybrid representations, with EMA or gradient-based updates; voxel estimators are faster but more prone to aliasing.For unbounded scenes, sampling can instead occur in a mapped space that samples more coarsely at greater distances.Source: §3.2 · pp. 4–5

3.3. Discussions

  • Uniform sampling is easiest but often least efficient; spatial skipping still samples occluded occupied regions, while PDF estimators sample throughout space and current approaches introduce aliasing.The cited aliasing sources are piecewise-linear transmittance assumptions and voxel representations.Source: §3.3 · p. 5
  • Efficient sampling implementations are tightly coupled to individual radiance-field codebases, making transferable implementations valuable for reducing reimplementation effort.The paper notes customized CUDA kernels and repository-specific proposal networks as examples.Source: §3.3 · p. 5
  • Occupancy grids and proposal networks are complementary: refining occupancy-grid transmittance with a proposal network can skip empty space and concentrate samples on surfaces.The combined approach is described as overcoming the proposal-network limitation of sampling the entire space.Source: §3.3 · p. 5

4. NerfAcc Toolbox

NerfAcc separates sampling from the radiance-field representation and packages advanced sampling in a flexible Python toolbox. Case studies show its use across NeRF variants, sampler choices, and combined sampling.

4. NerfAcc Toolbox

  • 1.5×–20× training speedups with minor code modifications are reported across NeRF-related methods, with slightly better performance in nearly all tested cases.The comparisons include official-codebase integrations and reimplementations.Source: §4 · p. 5, Table 2 · p. 6, §4.3 · p. 7
  • NerfAcc decouples sampling from radiance-field representation and unifies sampling approaches as transmittance estimators for importance sampling.The toolbox incorporates Occupancy Grid and Proposal Network sampling.Source: §4.2 · p. 6

4.2. Implementation Details

  • Samples are represented as ray intervals and packed tensors, supporting conical rays, detached sampling, and variable valid-sample counts without storing invalid samples.Filtering samples with transmittance below 10^-4 is about 10× faster than keeping all samples in the differentiable graph, with almost no rendering-quality influence.Source: §4.2 · pp. 6–7

4.3. Case Studies

  • The toolbox supports MLP- and voxel-based static fields on bounded and unbounded scenes, including pure-Python Instant-NGP with slightly better performance than its official CUDA implementation.The comparison is reported in Table 2a.Source: §4.3 · p. 8
  • For dynamic NeRFs, the occupancy grid is shared across frames and marks maximum opacity over time; the authors note this is not optimal but remains efficient on datasets with limited movement.Source: §4.3 · p. 8
  • For BARF camera optimization, spatial-skipping sampling is reported to speed training and improve image quality and camera-pose reconstruction.The authors attribute these improvements to sparsity in the sampling procedure.Source: §4.3 · p. 8
  • Occupancy-grid sampling suits NeRF-Synthetic, while proposal-network sampling performs better on 360 data, where bounding boxes and less skippable empty space limit occupancy grids.The comparison uses Instant-NGP radiance fields and the same training recipes.Source: §4.3 · p. 8

4.4. Combined Sampling

  • On NeRF-Synthetic, stacking an occupancy grid on a proposal network raises quality from 31.40dB to 32.35dB and cuts training time from 5.2min to 4.3min versus proposal-only sampling.The combined method also reduces ray count and shrinks the near-far plane for remaining rays.Source: §4.4 · p. 8

5. Conclusions

The conclusion emphasizes advanced sampling as a way to improve NeRF optimization and rendering efficiency while maintaining high-quality results. NerfAcc offers a flexible Python toolbox for incorporating these methods.

  • Advanced sampling significantly speeds training across various recent NeRF papers while maintaining high-quality results.Source: §5 · p. 8
  • NerfAcc is a flexible Python toolbox for incorporating advanced sampling methods into NeRF-related methods.Source: §5 · p. 8
  • The results point to further research potential for improving NeRF and related techniques through advanced sampling strategies.Source: §5 · p. 8

A. Implementation Details for Case Studies

This appendix section describes using NerfAcc to enhance multiple NeRFs and provides details on adapting it to different models.

A.1. Static NeRFs.

The static-NeRF case studies report acceleration for vanilla NeRF and TensoRF, and compare Python-based Instant-NGP results with its official implementation.

A.1. Static NeRFs.

  • 20× speedup and +0.5dB image-quality improvement are reported for vanilla NeRF using occupancy-grid spatial skipping.The empty space in NeRF-Synthetic permits 1,024 samples per ray without memory issues.Source: §A.1 · p. 11
  • 1.5× speedup is reported for TensoRF on both NeRF-Synthetic and Tank-and-Temple datasets.The authors conjecture the improvement comes from updating the transmittance estimator more frequently using a running density-field average.Source: §A.1 · p. 11
  • On NeRF-Synthetic, occupancy-grid Instant-NGP matches the official implementation's training speed and improves performance by +0.2dB; proposal-network sampling gives significantly better 360-dataset results.The NerfAcc sampling logic allows the training pipeline to be implemented in Python on PyTorch rather than pure CUDA.Source: §A.1 · p. 11

A.2. Dynamic NeRFs.

NerfAcc accelerates several dynamic NeRF methods, with gains varying by method and dataset. The experiments also note an implementation caveat for T-NeRF and a potential benefit of time-conditioned proposal networks on real bounded scenes.

A.2. Dynamic NeRFs.

  • 20× training speedup and +3.5dB performance improvement were achieved for T-NeRF on D-NeRF.The experiment used the authors’ re-implementation of the T-NeRF model and training recipe.Source: §A.2 · p. 11
  • Replacing K-Planes’ proposal-based transmittance estimator with an occupancy grid yielded around 1.6× speedup on D-NeRF.The authors suggest D-NeRF’s sparsity makes it more suitable for spatial skipping.Source: §A.2 · p. 11
  • TiNeuVox training sped up 2.8× on D-NeRF and 1.7× on HyperNeRF datasets with NerfAcc.The occupancy grid addresses wasteful marching in the main density grid; a time-conditioned proposal network achieved similar performance on real bounded HyperNeRF scenes.Source: §A.2 · p. 11

A.3. Camera Optimization.

Adding NerfAcc’s occupancy grid to BARF’s uniform near-far sampling produced faster training, better image quality, and lower camera registration error.

A.3. Camera Optimization.

  • 4× training speedup followed from integrating NerfAcc’s occupancy grid into BARF.BARF originally samples uniformly within each ray’s near-far range.Source: §A.3 · p. 11
  • +1.3dB image quality accompanied the occupancy-grid integration into BARF.Source: §A.3 · p. 11
  • Camera registration error was roughly 2× lower with the occupancy grid in BARF.Source: §A.3 · p. 11

B. Hyper-parameter Space

The Figure 5 experiments sweep sampling-method hyperparameters using random combinations drawn from reasonable ranges for each dataset.

B. Hyper-parameter Space

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