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Fast Dynamic Radiance Fields with Time-Aware Neural Voxels

Jiemin Fang, Taoran Yi, Xinggang Wang, Lingxi Xie, Xiaopeng Zhang, Wenyu Liu, Matthias Nießner, Qi Tian

arXiv:2205.15285v2cs.CVcs.GR

TL;DR

Dynamic NeRFs are costly to optimize, while explicit voxel features must encode time and accommodate motions at different scales. TiNeuVox combines time-aware voxel features, compact deformation, temporal enhancement, and multi-distance interpolation; it trains in 8 minutes with 8 MB while achieving similar or better rendering performance than prior dynamic NeRF methods.

  • Problem

    Dynamic NeRF optimization is expensive, and voxel features must represent temporal information and point motions at different scales.

  • Method

    TiNeuVox uses time-aware voxel features, a tiny coordinate-deformation network, radiance-network temporal enhancement, and multi-distance interpolation.

  • Results

    8 minutes and 8 MB achieve similar or better rendering performance than previous dynamic NeRF methods, with training 150× faster than D-NeRF and 192× faster than Hyper-NeRF.

  • Takeaways & Limitations

    TiNeuVox provides a fast, low-storage framework for dynamic radiance fields while maintaining high rendering quality on synthetic and real scenes.

  • Takeaways & Limitations

    The method produces plausible but limited results for long-distance motion and shares existing neural-representation limitations in handling specularities.

Abstract

from arXiv · show

Neural radiance fields (NeRF) have shown great success in modeling 3D scenes and synthesizing novel-view images. However, most previous NeRF methods take much time to optimize one single scene. Explicit data structures, e.g. voxel features, show great potential to accelerate the training process. However, voxel features face two big challenges to be applied to dynamic scenes, i.e. modeling temporal information and capturing different scales of point motions. We propose a radiance field framework by representing scenes with time-aware voxel features, named as TiNeuVox. A tiny coordinate deformation network is introduced to model coarse motion trajectories and temporal information is further enhanced in the radiance network. A multi-distance interpolation method is proposed and applied on voxel features to model both small and large motions. Our framework significantly accelerates the optimization of dynamic radiance fields while maintaining high rendering quality. Empirical evaluation is performed on both synthetic and real scenes. Our TiNeuVox completes training with only 8 minutes and 8-MB storage cost while showing similar or even better rendering performance than previous dynamic NeRF methods.

1 INTRODUCTION

TiNeuVox addresses the high cost of dynamic NeRF optimization by combining time-aware voxel features with compact motion modeling and multi-distance interpolation. The method reports fast training, low storage, and comparable or better rendering quality.

  • Motivation: Dynamic NeRF methods require substantial training and inference cost because they commonly add a deformation network comparable in scale to the radiance network.Conventional NeRF optimization generally takes dozens of hours, and dynamic-field deformation increases both training and inference costs.
  • Motivation: Explicit structures accelerate NeRFs but dynamic scenes require temporal encoding and must represent motions at different spatial scales.Adding a time dimension to voxel grids multiplies storage by N_t, while single-scale grids cannot simultaneously capture large motions and small-motion details.
  • Approach: TiNeuVox represents dynamic scenes with time-aware voxel features and a highly compressed deformation network that maps points into a coarse canonical space.Time information is further supplied to the radiance network to reduce deviations introduced by coordinate mapping.
  • Approach: Multi-distance interpolation uses voxel features sampled at multiple distances to model both small and large motions with one voxel-feature resolution.Near voxels represent small motions, while farther voxels provide information over larger regions.
  • Results: 8 minutes and 8 MB yield similar or better rendering quality than prior dynamic NeRF methods, with training reported as 150× faster than D-NeRF and 192× faster than Hyper-NeRF.The evaluation covers both synthetic and real scenes.

2 RELATED WORKS

The related work positions TiNeuVox at the intersection of dynamic-scene NeRFs and explicit voxel-grid acceleration. It extends voxel-based acceleration from static to dynamic scenes while targeting temporal representation and motion-scale challenges.

  • Neural rendering: NeRF research has expanded from basic scene representation toward anti-aliasing, camera optimization, unbounded scenes, and reconstruction from unstructured images.These works improve different aspects of neural rendering beyond the original NeRF formulation.
  • Dynamic scenes: Dynamic-scene rendering focuses on temporal information encoding, using time dimensions or deformation-based representations to model scene changes.Some methods also require geometry regularization and additional data modalities because non-rigid scene structure lacks strong prior knowledge.
  • Acceleration: Rendering acceleration reduces inference latency through improved sampling strategies or efficient rendering techniques.These approaches address the cost of sampling and processing many points along each ray.
  • Acceleration: Convergence acceleration methods reduce training cost through generalization, external depth information, or explicit voxel-grid representations.Voxel-optimizable approaches had previously focused on static scenes.
  • TiNeuVox: TiNeuVox introduces optimizable explicit voxel features into dynamic scenes by encoding temporal information into time-aware neural voxels.It reports similar or better rendering performance than previous dynamic NeRF methods while reducing training time from days to 8 minutes.

3 METHOD

TiNeuVox combines explicit neural voxels, compact temporal deformation, multi-distance interpolation, and a radiance network to represent and render dynamic scenes. Its pipeline samples and encodes points, predicts density and color, and optimizes these components through volume-rendering losses.

  • Preliminaries: NeRF maps spatial coordinates and view directions to color and density, then renders pixel colors by accumulating samples along camera rays.Ray samples span predefined near and far bounds and are combined through classical volume rendering.
  • Preliminaries: Positional encoding maps inputs to periodic features because raw coordinates and view directions alone do not depict scene details.The highest encoded frequency is controlled by the hyperparameter L.
  • Neural voxels: Neural voxels store features on an explicit grid; features at a point are queried from eight surrounding vertices and trilinearly interpolated before neural prediction.Explicit voxel features are introduced to reduce the optimization time of otherwise implicit radiance fields.
  • Multi-Distance Interpolation: Multi-distance interpolation concatenates trilinearly interpolated features from voxel grids with multiple sampling strides.Near voxels model small motions, while farther voxels perceive motion over larger regions; positional encoding is applied before neural inference.
  • Temporal encoding: A compact deformation network maps each point and its encoded time embedding to shifted coordinates before voxel querying.The deformation network uses a three-layer MLP and is compressed in width and depth to reduce per-sample computation.
  • Overall framework: The radiance network concatenates interpolated voxel features, encoded time, original coordinates, and view direction to predict density and color.The framework first encodes time, deforms coordinates, performs multi-distance voxel interpolation, and then applies a narrow, shallow radiance network.
  • Optimization: Training samples points evenly along each ray and optimizes voxel and network parameters using color reconstruction with additional regularization losses.A density threshold filters points before view-dependent color prediction to reduce inference cost with little reported quality impact.

4 EXPERIMENTS

TiNeuVox is evaluated on synthetic and real dynamic scenes, including quantitative, qualitative, and ablation studies. It achieves fast training and low storage while maintaining competitive rendering quality, and its temporal encoding and multi-distance interpolation components are important.

  • Evaluation results: TiNeuVox-S uses 8 MB, while TiNeuVox-B uses 48 MB, with TiNeuVox-S similar in storage to pure-implicit D-NeRF.TiNeuVox-B is reported as much smaller than previous explicit methods.
  • Evaluation results: On real scenes, TiNeuVox takes 192× less training time than HyperNeRF while achieving similar rendering performance.Rendered images are slightly more blurred than HyperNeRF, despite comparable performance.
  • Ablation studies: All three temporal-information components—coordinate deformation, temporal enhancement, and neural time embeddings—are reported as critical to final rendering performance.The finding comes from the TiNeuVox-B ablation study.
  • Ablation studies: Multi-distance interpolation improves PSNR by 0.467 at 100^3 resolution, 0.874 at 160^3, and 1.593 at 256^3.The larger gains at higher resolutions are attributed to smaller grid regions and incomplete motion capture without MDI.
  • Ablation studies: Distant voxel samples receive larger gradients for large-motion points, whereas small-motion points prefer nearer samples.The visualization uses red for distant-sample importance and yellow for near-sample importance.

5 DISCUSSION AND CONCLUSION

The paper concludes that TiNeuVox provides a fast, low-storage dynamic radiance-field framework using time-aware neural voxels. It also identifies unresolved challenges including specularities, complicated real scenes, motion priors, and further compression.

  • Limitations & future works: Future work includes neighboring-frame relations, spatial or temporal network partitioning, and handling long-distance motion more fully.Preliminary experiments on the NSFF truck scene produced plausible quality but left opportunities for improvement.
  • Limitations & future works: Specularities remain difficult, and the approach shares limitations with existing neural representation methods in handling reflections.The paper points to reflection-specific methods as a potential orthogonal direction.
  • Limitations & future works: Further topics include regularization for complicated real scenes, geometric or motion priors, and compression or pruning techniques.Examples include voxel hashing and tensor decomposition.
  • Conclusion: TiNeuVox encodes temporal information with a compressed deformation network and radiance-network enhancement, while multi-distance interpolation models motions at varied scales.The framework targets dynamic scenes and uses time-aware neural voxels.

A.1 More Ablation Studies

Additional ablations examine positional encoding, network width, voxel dimensions, and multi-distance interpolation. They show trade-offs between rendering quality, model capacity, training time, and storage cost.

  • Positional Encoding on Neural Voxels: Removing positional encoding from neural voxels causes dramatic performance degradation and missing details, while increasing channels to 12 still performs worse than 6 channels with 2-frequency encoding.The 12-channel setting increases storage to 94 MB.
  • Positional Encoding on Neural Voxels: Positional encoding models details with limited voxel channel dimensions without adding parameters.Using a larger frequency number of 4 produces a worse result in the reported experiment.
  • Coordinate Deformation: Coordinate deformation maps points from a canonical space into a specified time space, such as t = 0.3.The displayed canonical space is not explicitly fixed to a particular time.
  • Multi-distance Interpolation: Multi-distance interpolation uses one voxel set instead of separate pyramid voxel grids for representing multiple distances.The comparison is framed as an ablation between separate pyramid grids and MDI.
  • Hidden Layer Widths: Larger hidden-layer MLP widths improve rendering performance but increase computation cost and training time.Implicit representations are evaluated for every sample along rays.
  • Neural Voxel Dimensions: Increasing neural-voxel channel dimensions can improve performance while slightly increasing time and storage cost.The 12-channel setting improves SSIM and LPIPS without obtaining a higher PSNR.

A.2 Visualization of Coordinate Deformation

The deformation network maps points along coarse motion trajectories, with small drift subsequently suppressed by multi-distance interpolation and temporal information enhancement.

  • Most points deform to the same corresponding position, although small drift remains.
  • The deformation network predicts only a coarse motion trajectory rather than fully eliminating point-mapping deviation.
  • Subsequent multi-distance interpolation and temporal information enhancement suppress or eliminate deformation-induced deviation.

A.3 Comparisons with Separate Pyramid Voxels Grids

TiNeuVox’s multi-distance interpolation uses one voxel set at multiple sample distances instead of separate pyramid grids, reducing storage while preserving rendering quality.

  • 13% smaller storage cost is achieved by multi-distance interpolation with one voxel set compared with separate pyramid voxel grids.Both approaches have similar time, while the single-set design produces the same rendering quality.
  • The separate-grid baseline constructs voxel sets at 160^3, 80^3, and 40^3 resolutions and concatenates their interpolated features.
  • TiNeuVox constructs only a 160^3 voxel set and interpolates three neural voxels from it using different sample distances.

A.4 Details of Additional Losses

Two additional losses stabilize training and improve foreground-background separation, together yielding a reported PSNR improvement.

  • The all-samples color loss supervises every sample with the target color to stabilize optimization and mitigate local minima early in training.Its loss weight is 10^-2 to avoid making all samples produce the same color.
  • The background entropy loss facilitates distinguishing foreground and background areas by encouraging focus on either region.
  • 1.2-PSNR improvement results from combining the all-samples color loss with the background entropy loss.

A.5 Neural Architectures

The paper presents separate architectures for deformation, time encoding, and radiance processing, and reports quantitative and qualitative evaluations across synthetic and real scenes.

  • The deformation network Φ_d and time-encoding network Φ_t architectures are shown in Figure 10, while the radiance network Φ_r is shown in Figure 11.
  • Per-scene quantitative results are reported for synthetic scenes in Table 9 and real scenes in Table 10.
  • Additional qualitative comparisons on synthetic scenes are provided in Figure 12.
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