Source-linked AI summary
EfficientNeRF: Efficient Neural Radiance Fields
Tao Hu, Shu Liu, Yilun Chen, Tiancheng Shen, Jiaya Jia
TL;DR
NeRF offers high-quality scene representation and novel-view synthesis but requires substantial per-scene training and per-image testing time. EfficientNeRF combines valid and pivotal sampling with NerfTree scene caching, reducing training time by over 88% and reaching over 200 FPS while maintaining competitive accuracy. The method still requires training from scratch for novel scenes, and its synthesis accuracy remains limited when using image priors.
Problem
NeRF’s high-quality novel-view synthesis requires costly training and testing, and prior methods had not significantly shortened both phases simultaneously.
Method
EfficientNeRF uses Valid Sampling and Pivotal Sampling during training, then caches coarse dense and fine sparse scene voxels with NerfTree during testing.
Results
Over 88% of computation and training time is reduced, and rendering speed exceeds 200 FPS while maintaining competitive accuracy.
Takeaways & Limitations
EfficientNeRF improves the practicality of NeRF-based 3D scene representation and novel-view synthesis by accelerating both training and testing.
Takeaways & Limitations
EfficientNeRF still requires training from scratch for novel scenes, and synthesis accuracy is limited when combining image priors with its efficient sampling.
Abstract
from arXiv · showhide
Neural Radiance Fields (NeRF) has been wildly applied to various tasks for its high-quality representation of 3D scenes. It takes long per-scene training time and per-image testing time. In this paper, we present EfficientNeRF as an efficient NeRF-based method to represent 3D scene and synthesize novel-view images. Although several ways exist to accelerate the training or testing process, it is still difficult to much reduce time for both phases simultaneously. We analyze the density and weight distribution of the sampled points then propose valid and pivotal sampling at the coarse and fine stage, respectively, to significantly improve sampling efficiency. In addition, we design a novel data structure to cache the whole scene during testing to accelerate the rendering speed. Overall, our method can reduce over 88\% of training time, reach rendering speed of over 200 FPS, while still achieving competitive accuracy. Experiments prove that our method promotes the practicality of NeRF in the real world and enables many applications.
1. Introduction
NeRF provides high-quality novel-view synthesis but is computationally expensive for both per-scene training and per-image testing. EfficientNeRF addresses both bottlenecks with selective sampling and scene caching, reducing training computation while accelerating rendering.
- Motivation: NeRF training can take 1–2 days and testing about 30 seconds per image for a 100-image, 800 × 800 scene.These costs impede fast practical applications.
- Motivation: Existing methods typically accelerate either training or testing, while no prior work significantly shortens both phases simultaneously.Training-acceleration methods may reduce synthesis accuracy, whereas testing-acceleration methods can retain heavy training costs.
- Method: EfficientNeRF uses Valid Sampling at the coarse stage and Pivotal Sampling at the fine stage to focus computation on important samples.Valid Sampling queries only samples with density above a threshold, while Pivotal Sampling concentrates fine samples near pivotal points.
- Method: NerfTree caches coarse dense and fine sparse scene representations so testing uses fast voxel queries instead of repeated MLP inference.The two-layer structure combines dense coarse voxels with sparse fine voxels.
2. Related Work
Prior work accelerates novel-view synthesis through voxel representations, image priors, sparse sampling, tiny MLPs, or cached scenes. These approaches expose a trade-off between training cost, inference speed, accuracy, memory, and testing overhead that EfficientNeRF targets jointly.
- Novel View Synthesis: Voxel grids support real-time synthesis but can require large memory, while MPI-based methods synthesize depth-wise images before α-compositing them into target views.These are alternative representations for novel-view synthesis.
- NeRF-based Applications: NeRF-based methods extend implicit scene representations to dynamic scenes, human bodies, controllable image generation, and high-resolution synthesis.Examples include deformation-based models, human-pose synthesis, GAN-based generators, and StyleNeRF at 1K resolution.
- Training Acceleration: Training-acceleration methods use image features or feature volumes to enable fast finetuning, but limited neighboring views can reduce synthesis accuracy.MVSNeRF is reported to produce high-quality images within 15-minute finetuning.
- Testing Acceleration: Testing-acceleration methods reduce sampling, use many small MLPs, or cache scenes, reaching speeds from around 1 FPS to over 200 FPS while retaining heavy training costs.NSVF reaches around 1 FPS, KiloNeRF exceeds 10 FPS, and PlenOctree and FastNeRF exceed 168 FPS and 200 FPS respectively.
- EfficientNeRF: EfficientNeRF combines selective training-time sampling with cached voxel representations to improve both per-scene training and per-image inference efficiency.Its overview uses coarse and fine voxel representations during training and testing.
3. Our Approach
EfficientNeRF combines a coarse-to-fine NeRF pipeline with valid and pivotal sampling, lightweight network design, and NerfTree scene caching to accelerate training and testing. Its sampling strategies exploit the small fraction of useful points, while NerfTree stores coarse dense and fine sparse voxel representations for fast inference.
- 3. Our Approach: EfficientNeRF targets accurate scene representation and novel-view synthesis with fast training and testing.The approach combines lightweight MLPs, valid and pivotal sampling, and NerfTree caching.
- 3.1. Background: Neural Radiance Fields: NeRF maps 5D point location and viewing direction to color and density, then renders sampled points along each ray.The representation uses an implicit function and volume rendering rather than explicit meshes or point clouds.
- 3.2. Network: The coarse-to-fine pipeline uses separate coarse and fine MLPs, but the coarse MLP is reducible because it mainly estimates coarse density.EfficientNeRF halves the coarse MLP depth and width while retaining the fine MLP, with little reported performance weakening.
- 3.3. Valid Sampling at the Coarse Stage: Only around 10%–20% of samples are valid because zero-density points do not affect the rendered color and can be skipped.The paper measures valid-sample proportions on the Realistic Synthetic and Real Forward-Facing datasets.
- 3.3. Valid Sampling at the Coarse Stage: Valid Sampling queries momentum density voxels and sends only samples above a density threshold to the coarse MLP.The voxels memorize scene-wide density values and are updated online with momentum; when valid samples comprise 10%, the theoretical coarse-stage acceleration is 10 times.
- 3.5. Represent Scene by NerfTree: NerfTree caches the scene offline in a two-depth structure containing coarse dense voxels and fine sparse voxels, eliminating coarse and fine MLPs during testing.The combined representation is designed to retain fast access while using less storage than dense voxels alone.
4. Experiments
Experiments show that EfficientNeRF maintains competitive image quality while accelerating both training and rendering across standard datasets and comparisons. Ablations attribute these gains to lightweight networks, efficient sampling, and NerfTree scene representation.
- Quantitative Comparison: 238.46 FPS rendering results from NerfTree’s fast querying of 3D attributes at target locations.NerfTree is the proposed scene representation used during testing.
- Trade-off between Accuracy and Speed: EfficientNeRF offers a speed–accuracy trade-off through four versions that vary coarse and fine sampling parameters.The variants N1–N4 are evaluated using PSNR–speed curves.
- Quantitative Comparison: EfficientNeRF achieves competitive PSNR/SSIM/LPIPS accuracy while accelerating both training and testing on the Realistic Synthetic and Real Forward-Facing datasets.The evaluation measures image quality, total training time, and rendering speed.
- Networks: A lightweight coarse MLP paired with a standard fine MLP preserves nearly the same accuracy while improving rendering speed.The experiments indicate that fine-MLP size mainly determines final synthesis quality.
- Efficient Modules: Valid sampling accelerates coarse processing, while pivotal sampling reduces training time and improves synthesis accuracy over original NeRF sampling.The efficient modules are evaluated on their contributions to training time and synthesis performance.
- Sampling Strategy: Efficient sampling accelerates NeRF-based methods while achieving comparable or better accuracy than uniform and original NeRF sampling.Uniform sampling is faster but less accurate, whereas original NeRF sampling is accurate but slow.
- Scene Representation: NerfTree performs best across memory consumption, caching time, and querying time among the compared scene representations.Dense voxels cache and query fastest but require 16.0 GB memory; NerfTree avoids much of that storage cost.
5. Conclusion
EfficientNeRF combines valid and pivotal sampling with NerfTree to accelerate accurate 3D scene representation and novel-view synthesis. It still requires training from scratch for novel scenes, and synthesis accuracy remains limited in that setting.
- EfficientNeRF uses valid sampling at the coarse stage and pivotal sampling at the fine stage to handle important samples efficiently.These strategies are designed to save substantial computation.
- NerfTree caches 3D scenes and yields faster testing than state-of-the-art NeRF-based methods.
- Novel scenes still require training from scratch, a limitation also shared by other state-of-the-art NeRF-based methods.
- Synthesis accuracy remains limited when combining image priors with efficient sampling, motivating improved generalization without novel-scene finetuning.