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Fourier PlenOctrees for Dynamic Radiance Field Rendering in Real-time
Liao Wang, Jiakai Zhang, Xinhang Liu, Fuqiang Zhao, Yanshun Zhang, Yingliang Zhang, Minye Wu, Lan Xu, Jingyi Yu
TL;DR
Dynamic free-view video requires efficient modeling and real-time rendering, beyond the static-scene focus of many neural radiance-field methods. The paper introduces Fourier PlenOctree by combining generalized NeRF, PlenOctree, volumetric fusion, and Fourier modeling, and reports high-quality real-time rendering with substantial acceleration. Its scope is constrained by input-capture costs, memory growth for faster motion or longer sequences, and difficulty handling large entity movements.
Problem
The paper addresses efficient neural modeling and real-time rendering for unseen dynamic scenes captured under the free-view video setting.
Method
Fourier PlenOctree combines generalized NeRF, coarse-to-fine spatial fusion, PlenOctree leaves, and Fourier coefficients for time-varying density and color attributes.
Results
3000 times faster than the original NeRF, FPO renders at 100fps with 800×800 resolution while producing high-quality dynamic-scene appearance results.
Takeaways & Limitations
FPO provides compact-memory dynamic scene modeling with real-time free-viewpoint synthesis and efficient fine-tuning.
Takeaways & Limitations
The method requires expensive dense inputs, higher storage and GPU memory for faster motion or more frames, and cannot handle large movements such as walking performers on the street.
Abstract
from arXiv · showhide
Implicit neural representations such as Neural Radiance Field (NeRF) have focused mainly on modeling static objects captured under multi-view settings where real-time rendering can be achieved with smart data structures, e.g., PlenOctree. In this paper, we present a novel Fourier PlenOctree (FPO) technique to tackle efficient neural modeling and real-time rendering of dynamic scenes captured under the free-view video (FVV) setting. The key idea in our FPO is a novel combination of generalized NeRF, PlenOctree representation, volumetric fusion and Fourier transform. To accelerate FPO construction, we present a novel coarse-to-fine fusion scheme that leverages the generalizable NeRF technique to generate the tree via spatial blending. To tackle dynamic scenes, we tailor the implicit network to model the Fourier coefficients of timevarying density and color attributes. Finally, we construct the FPO and train the Fourier coefficients directly on the leaves of a union PlenOctree structure of the dynamic sequence. We show that the resulting FPO enables compact memory overload to handle dynamic objects and supports efficient fine-tuning. Extensive experiments show that the proposed method is 3000 times faster than the original NeRF and achieves over an order of magnitude acceleration over SOTA while preserving high visual quality for the free-viewpoint rendering of unseen dynamic scenes.
1. Introduction
Free-viewpoint video applications require fast, photorealistic novel-view rendering, but neural rendering has largely focused on static scenes. The paper introduces Fourier PlenOctree for efficient modeling and real-time rendering of unseen dynamic objects.
- Motivation: Free-viewpoint video supports controllable viewing experiences in interactive applications such as telepresence and VR.These applications require fast generation and real-time rendering at new viewpoints with high photorealism.
- Motivation: Existing neural rendering methods primarily model static objects using data structures that enable real-time rendering.The background describes neural volumetric modeling and smart representations for static scenes.
- Proposed approach: Fourier PlenOctree combines generalized NeRF, PlenOctree representation, volumetric fusion, and Fourier transform for dynamic scene representation.This combination targets efficient neural modeling and real-time rendering of unseen dynamic objects.
- Proposed approach: The method uses a coarse-to-fine scheme with generalizable NeRF to construct PlenOctrees efficiently.The scheme is designed for fast PlenOctree generation and supports FPO construction.
- Proposed approach: Fourier PlenOctree represents time-varying density and plenoptic functions through Fourier coefficients and supports real-time synthesis and efficient fine-tuning.The representation is described as having compact memory overhead while preserving perceptive details.
2. Related work
Prior work developed compact or real-time representations for static novel-view synthesis and neural methods for dynamic scenes, but dynamic radiance fields remained costly to train and render. Existing dynamic approaches also rely on motion mappings or object-specific models with important limitations.
- Static-scene synthesis: Novel-view synthesis methods construct geometric or image-based 3D representations that allow rendering from new viewpoints.Mesh and volume representations offer different trade-offs between compactness, topology handling, optimization, and rendering speed.
- Static-scene synthesis: NeRF combines implicit representation with volumetric rendering and produces view-dependent effects, but the cited neural representations handle only static scenes.This establishes the static-scene focus of foundational neural radiance-field methods.
- Dynamic-scene synthesis: Dynamic-scene methods address moving objects and illumination changes using camera, silhouette, stereo, segmentation, or photometric inputs.The related work surveys multiple reconstruction settings for dynamic objects.
- Dynamic-scene synthesis: Dynamic neural radiance fields commonly learn mappings from timestamped current scenes to a canonical scene before regressing the canonical radiance field.The cited methods still suffer from long training and rendering times.
- NeRF acceleration: Real-time acceleration methods include feature-based or meta-learned priors, spherical-basis MPI extensions, NeRF distillation, and octree representations.The cited approaches target faster rendering, but the paper identifies an unresolved dynamic-scene challenge.
3. Generalized PlenOctree Fusion
Generalized PlenOctree Fusion accelerates PlenOctree construction by querying a generalized NeRF and spatially blending information from multiple views. A coarse-to-fine process removes redundant leaves and refines leaf values using dense rendered views.
- Motivation: PlenOctree Fusion addresses the time-consuming acquisition of spherical-harmonic coefficients and densities despite fast PlenOctree rendering.It replaces naive gradient descent updates with fusion using a generalized NeRF.
- Fusion pipeline: A generalized neural rendering network infers an implicit volume from adjacent views, allowing direct queries of colors and densities at PlenOctree leaves without per-scene training.Multiple target views are sampled because each query is view-dependent, and their local trees are fused.
- Coarse stage: The coarse stage initializes voxel-grid leaves inside a visual hull and fills them using predictions from six sparse views.Silhouettes produce the visual hull, while sampled viewing directions provide densities and colors for each leaf.
- Fine stage: The fine stage renders 100 dense views, queries points with transmittance T_i > 1e-3, and fuses updates to refine the PlenOctree.The sparse initialization covers all points, while dense views query only about 1% of points.
- Results: Fusion with the generalized NeRF avoids many optimization iterations and produces a static-scene PlenOctree within 60 seconds.The reported generation speed is substantially faster than iterative refinement alone.
4. Fourier PlenOctree
Fourier PlenOctree represents dynamic scenes by storing Fourier coefficients for time-varying density and spherical-harmonic appearance in an octree. A generalized fusion pipeline constructs the shared structure efficiently, after which differentiable fine-tuning improves rendering quality.
- 4.1. 4D Scene Representation in Frequency Domain: Fourier PlenOctree extends PlenOctree to dynamic scenes by compressing time-varying density and appearance information in the frequency domain.Each leaf stores Fourier coefficients for density and spherical-harmonic coefficients, enabling free-viewpoint video rendering.
- 4.1. 4D Scene Representation in Frequency Domain: The mapping function predicts density and spherical-harmonic Fourier coefficients from a 4D scene sample point (x, y, z, t).The density coefficient vector has n1 entries, while appearance coefficients include the spherical-harmonic dimensions and RGB channels.
- 4.2. Fourier PlenOctree Generation: Generalized PlenOctree Fusion uses coarse-to-fine spatial blending to generate dynamic-scene trees without fitting the continual implicit function from scratch.The naive implicit reconstruction takes about 1-2 days, motivating the fusion-based generation procedure.
- 4.2. Fourier PlenOctree Generation: For each leaf, corresponding per-frame PlenOctree values are transformed with the DFT and stored in a unified Fourier PlenOctree structure.The shared octree structure is required so plenoptic functions at the same spatial positions can be analyzed across frames.
- 4.3. Fourier PlenOctree Fine-tuning: Differentiable DFT-based fine-tuning uses ground-truth images and is faster than optimizing the Fourier NeRF-SH implicit representation.The explicit Fourier PlenOctree representation is easier to optimize than an MLP-based implicit representation.
5. Experimental Results
FPO achieves high-quality real-time rendering for unseen dynamic scenes while offering fast construction, efficient fine-tuning, and a favorable storage-quality balance.
- Overall performance: 100fps rendering at 800×800 resolution is 3000 times faster than the original NeRF.Experiments use 60 views and 60 frames, with reconstruction taking about 2 hours on one RTX3090 GPU.
- Overall performance: FPO produces photorealistic, sharp free-viewpoint renderings and supports real-time dynamic-scene rendering.The comparison includes Neural Volumes, iButter, ST-NeRF, and Neural Body using the same training dataset.
- Quantitative comparison: FPO outperforms competing methods on PSNR, SSIM, LPIPS, and MAE, while achieving the best FPS and second-best training or fine-tuning time.The evaluation uses 90% of captured views for training and 10% for testing.
- Ablation study: 10-minute fine-tuning removes detail blurring and artifacts, while extending fine-tuning to 10 hours yields only slight improvement.The qualitative and quantitative evaluations report the same pattern.
- Ablation study: The Fourier setting n1 = 31, n2 = 5 provides the best balance among rendering accuracy, time, and storage.Higher Fourier dimensions increase storage and reduce rendering speed without significant quality gains.
- Ablation study: With limited memory, DFT significantly improves quality while FPO uses minimal storage and the least training time for high-fidelity dynamic rendering.The comparison includes Fourier and non-Fourier variants under limited and unlimited memory.
6. Discussion
The discussion identifies input, memory, motion, and initialization constraints that bound FPO’s practical applicability.
- Limitations: FPO still requires dense inputs, making the capture settings expensive and difficult to construct.This limitation applies to both static and dynamic scenes.
- Limitations: Compared with MLP-based implicit representations, FPO requires more storage and GPU memory.Longer sequences or faster motion require higher Fourier dimensions, increasing these requirements.
- Limitations: FPO cannot handle large entity movements such as walking performers on the street.The approach is inefficient because it initializes Fourier PlenOctrees using the union of visual hulls.