Source-linked AI summary
InfoNeRF: Ray Entropy Minimization for Few-Shot Neural Volume Rendering
Mijeong Kim, Seonguk Seo, Bohyung Han
TL;DR
Few-shot novel view synthesis is challenged by limited viewpoints, inconsistent reconstructions, and overfitting to seen views. InfoNeRF addresses these issues with ray entropy minimization and ray information gain reduction, achieving consistently improved performance with large margins on multiple standard benchmarks.
Problem
Few-shot novel view synthesis lacks sufficient viewpoint coverage, causing inconsistent reconstructions and overfitting to seen views that can produce degenerate solutions.
Method
InfoNeRF regularizes neural volume rendering with ray entropy minimization and ray information gain reduction, using opacity-based ray probabilities for entropy computation.
Results
InfoNeRF consistently outperforms baseline algorithms across all reported metrics with considerable margins and lower standard deviations on the Realistic Synthetic 360° benchmark.
Takeaways & Limitations
The proposed regularization alleviates reconstruction inconsistency across views and prevents degenerate solutions by overfitting despite its simplicity.
Takeaways & Limitations
When training images have similar viewpoints, models remain prone to overfit seen images and fail to generalize to unseen views.
Abstract
from arXiv · showhide
We present an information-theoretic regularization technique for few-shot novel view synthesis based on neural implicit representation. The proposed approach minimizes potential reconstruction inconsistency that happens due to insufficient viewpoints by imposing the entropy constraint of the density in each ray. In addition, to alleviate the potential degenerate issue when all training images are acquired from almost redundant viewpoints, we further incorporate the spatially smoothness constraint into the estimated images by restricting information gains from a pair of rays with slightly different viewpoints. The main idea of our algorithm is to make reconstructed scenes compact along individual rays and consistent across rays in the neighborhood. The proposed regularizers can be plugged into most of existing neural volume rendering techniques based on NeRF in a straightforward way. Despite its simplicity, we achieve consistently improved performance compared to existing neural view synthesis methods by large margins on multiple standard benchmarks.
1. Introduction
Few-shot novel view synthesis seeks to reconstruct and render scenes from limited observations without scene priors, but existing methods face inconsistent reconstruction and overfitting. InfoNeRF addresses these issues with ray entropy minimization and ray information gain reduction, while remaining broadly applicable and performing strongly on benchmarks.
- NeRF-based novel view synthesis is constrained by its usual requirement for many images captured from multiple calibrated cameras.
- Few-shot prior-free synthesis uses limited training images without object-category, semantic, or other scene-specific prior information.
- InfoNeRF targets inconsistent reconstruction and overfitting to seen views, which produce artifacts and degenerate or trivial solutions.
- InfoNeRF introduces ray entropy minimization and ray information gain reduction as two information-theoretic regularization schemes.
- The regularization requires no external data structures or additional learnable parameters and can be applied to various neural volume rendering algorithms.
- InfoNeRF is reported to achieve outstanding performance on several standard few-shot novel view synthesis benchmarks and is presented as the first prior-free NeRF variant for wide-baseline image datasets.
2. Related Work
Related few-shot view synthesis methods use depth, semantic features, scene priors, or explicit representations to compensate for sparse observations. These strategies introduce supervision, external modules, pretrained encoders, or representations that InfoNeRF does not require.
- Depth-guided few-shot methods use depth for view synthesis and reconstruction but may require depth supervision or external estimation modules.
- External depth estimates can suffer large projection errors when their predicted depth is incorrect.
- Feature-semantic approaches add image encoders or semantic consistency objectives to provide scene priors for few-shot synthesis.
- Some methods focus on particular object classes, while others exploit features from seen images or require narrow-baseline correspondence estimation.
- Explicit volume-rendering representations impose sparsity through octrees or voxel-grid regularization, although they do not directly address few-shot novel-view synthesis.
3. Preliminaries: NeRF
NeRF represents scenes with an implicit neural function that predicts density and color, then renders pixels by integrating sampled values along rays. Its differentiable hierarchical sampling and photometric objective enable optimization, but conventional NeRF generally needs many calibrated images.
- NeRF maps a 3D point and viewing direction through an MLP to volume density and emitted RGB color.
- Rendered ray colors are approximated by quadrature over sampled points because continuous outputs along the ray are not directly observable.
- Accumulated transmittance weights each sample's contribution to the rendered pixel color along the ray.
- NeRF samples points hierarchically, using uniform sampling followed by density-based importance sampling to improve rendering efficiency.
- The neural radiance field is optimized with a differentiable RGB reconstruction loss over a set of rays.
- Positional encoding maps coordinates to a higher-dimensional space to help the MLP represent high-frequency scenes.
- Although NeRF produces photorealistic results, its typical need for many densely captured calibrated images motivates few-shot alternatives.
4. Proposed Method
InfoNeRF addresses noisy, inconsistent few-shot reconstructions with ray-wise entropy minimization and neighborhood consistency regularization. These constraints compact density along rays and encourage smooth predictions across nearby viewpoints, while remaining efficient and broadly applicable.
- Motivation: Few-shot NeRF produces noisy depth and rendering, and can overfit seen views so strongly that slight viewpoint changes yield different results.Similar training viewpoints especially promote degenerate solutions and failure on unseen views.
- Ray entropy minimization: InfoNeRF minimizes each ray’s density entropy to impose sparsity on the reconstructed scene and reduce reconstruction inconsistency.The method assumes only a small subset of sampled points along a ray typically hits objects or background, while the rest likely observes noise.
- Ray entropy minimization: Ray density is normalized from opacity α_i = 1 − exp(−σ_iδ_i), accounting for irregular sampling intervals rather than using density alone.The normalized distribution is defined over sampled points along each ray.
- Ray entropy minimization: Entropy regularization uses rays from training and randomly sampled unseen viewpoints, including rays without pixel-color ground truth.The method masks low-density rays to avoid forcing non-hitting rays toward low entropy, and entropy computation adds negligible cost because σ and δ are already available.
- Efficiency and applicability: InfoNeRF’s ray-wise entropy formulation is more efficient than methods based on 3D volume entropy because it estimates entropy through one-dimensional ray sampling.The regularizers require no voxels, meshes, additional learnable parameters, or external data structures and can be applied to varied neural volume-rendering algorithms.
- Limitation: Entropy regularization helps when training viewpoints are sufficiently diverse, but similar viewpoints can still cause overfitting and failure to generalize to unseen views.The authors attribute this limitation to insufficiently diverse observations enabling degenerate or trivial solutions.
- Information gain reduction: InfoNeRF minimizes KL divergence between density distributions on an observed ray and a nearby-view ray to enforce spatial smoothness.The nearby ray is generated by slightly rotating the camera pose within −5° to 5°.
- Training objective: The total training loss combines RGB reconstruction, entropy, and KL regularization terms, with λ1 and λ2 balancing the regularizers.RGB reconstruction uses rays from training images with pixel-level ground truth, unlike the entropy and KL terms, which can use unobserved viewpoints.
5. Experiments
InfoNeRF is evaluated on standard few-shot novel-view benchmarks using limited training views, with comparisons, ablations, and robustness analyses. It consistently improves reconstruction quality, while each regularizer contributes differently depending on viewpoint diversity.
- Benchmarks: Experiments cover Realistic Synthetic 360°, ZJU-MoCap, and DTU, using four, four, and three training views respectively.The evaluation uses calibrated synthetic, human-motion, and real-world multi-view datasets.
- Realistic Synthetic 360°: InfoNeRF consistently outperforms baseline algorithms across all reported metrics on Realistic Synthetic 360° with considerable margins and lower standard deviations.Scene-level PSNR results also show significant gains across all eight scenes.
- Realistic Synthetic 360°: InfoNeRF produces higher-quality novel-view renderings and depth maps than the compared methods, which often fail to reconstruct 3D structure accurately.DietNeRF additionally exhibits color distortion attributed to its high-level semantic consistency loss.
- ZJU-MoCap: InfoNeRF achieves the best performance among methods without scene priors on ZJU-MoCap and particularly accurate qualitative reconstructions.Neural Body uses a pretrained SMPL human-body model and therefore serves as a prior-based upper-bound comparison.
- DTU: On DTU, InfoNeRF achieves outstanding performance compared with NeRF, whereas PixelNeRF performs highest by exploiting dense-view dataset pretraining.Evaluation uses masks of visible scene parts because prior-free models cannot reconstruct invisible regions.
- Analysis: Increasing unseen-view rays gradually alleviates reconstruction inconsistency, but benefits saturate above 1,024 rays.On DTU, entropy minimization improves PSNR but can worsen SSIM; information-gain reduction counteracts overfitting when training views have similar viewpoints.
- Analysis: InfoNeRF improves over NeRF across all metrics through eight training views, after which its benefit saturates as additional views reduce reconstruction uncertainty.The information-gain reduction loss is most useful for similar viewpoints, while entropy minimization contributes more when viewpoint variation is substantial.
6. Conclusion
InfoNeRF uses ray entropy minimization and information gain reduction to address noisy, inconsistent, and overfit few-shot reconstructions. These losses reduce both measured ray entropy and information gain relative to few-shot NeRF.
- Conclusion: InfoNeRF combines ray entropy minimization and ray information gain reduction to alleviate reconstruction inconsistency and overfitting across views.The two regularizers target noisy or artifact-prone reconstructions and degenerate solutions caused by overfitting to seen views.
- Conclusion: The method achieves outstanding performance on multiple standard few-shot novel view synthesis benchmarks.The conclusion also reports extensive analysis of the approach.
- Conclusion: Few-shot NeRF has higher ray entropy and information gain than NeRF trained with 100 views, indicating noisier rendered images.The comparison motivates reducing both quantities during training.
- Conclusion: InfoNeRF with four views reduces both ray entropy and information gain relative to few-shot NeRF.Figure A compares these quantities across NeRF with 100 views, NeRF with 4 views, and InfoNeRF with 4 views.
B. Experiments on More Complex Scenarios
InfoNeRF is evaluated on LLFF, a complex real-world dataset with eight handheld-cellphone scenes, using only two training views per scene. It outperforms the baseline by large margins and provides better visual quality in the reported comparisons.
- Experiments on More Complex Scenarios: LLFF contains 8 natural scenes captured by a handheld cellphone, with 20 to 62 images per scene.The evaluation follows the standard protocol, holding out 1/8 of each scene’s images for testing.
- Experiments on More Complex Scenarios: InfoNeRF is trained with 2 sampled views per LLFF scene while 1/8 of the images are held out as test sets.This tests the method in a more challenging real-world few-shot setting.
- Experiments on More Complex Scenarios: InfoNeRF outperforms the baseline by large margins on LLFF in the 2-view setting.Table A reports the quantitative comparison.
- Experiments on More Complex Scenarios: Figure B compares qualitative results for the Flower, T-Rex, Orchids, and Fern LLFF scenes.The passage states that InfoNeRF provides better visual quality in these comparisons.
C. Role of LKL in Narrow-Baseline Data
The narrow-baseline experiment compares four-view Lego training sets with narrow and wide viewpoint spacing. The combination of LKL and ray entropy loss is effective for the narrow-baseline setting.
- Role of LKL in Narrow-Baseline Data: The Lego experiment constructs narrow- and wide-baseline four-view training sets to test viewpoint variation.Figure C illustrates the two sampling settings from the Realistic Synthetic 360° dataset.
- Role of LKL in Narrow-Baseline Data: LKL is intended to prevent overfitting, especially when training images have narrow baselines.The loss is particularly helpful for the DTU dataset according to the experiment description.
- Role of LKL in Narrow-Baseline Data: The combination of LKL and ray entropy loss is effective for the narrow-baseline Lego setting.Table B reports the effects of LKL under viewpoint variation.
- Role of LKL in Narrow-Baseline Data: Figure D provides a qualitative comparison of reconstructed Lego images in the narrow-baseline four-view setting.The figure focuses on the narrow-baseline reconstruction quality.
D. Robustness to the Number of Training Views
Across varying numbers of training views, InfoNeRF maintains consistent improvements on PSNR, SSIM, and LPIPS, including the 2-view setting. Its regularization also transfers to PixelNeRF and mipNeRF, improving both over their baselines.
- InfoNeRF and its baseline show a consistent performance tendency across different numbers of training views on PSNR, SSIM, and LPIPS.
- InfoNeRF is qualitatively evaluated in the HotDog scene using only 2 training views.
- InfoPixelNeRF and InfoMipNeRF apply the proposed regularization to PixelNeRF and mipNeRF, respectively, and improve over their corresponding baselines on all metrics.
F. Per-Scene Breakdown on the Realistic Synthetic 360◦Dataset
InfoNeRF delivers consistent, non-trivial gains across individual Realistic Synthetic 360° scenes and all reported image-quality metrics, alongside stronger qualitative rendering and depth reconstruction.
- InfoNeRF achieves consistent and non-trivial improvement over its baselines across individual scenes in PSNR, SSIM, and LPIPS.
- The per-scene breakdown reports average PSNR, SSIM, and LPIPS values with standard deviations for the 4-view setting.
- InfoNeRF provides distinguished rendering quality across Lego, Chair, Drums, Ficus, HotDog, and Materials, with depth maps showing clear boundaries and fine details.
- On ZJU-MoCap, InfoNeRF renders most human bodies with competitive quality to the prior-based algorithm NB in the 4-view setting.
H. Potential Negative Societal Impact & Limitations
The authors identify vulnerability to adversarial attacks as a potential negative impact of the few-shot view-synthesis algorithm.
- The algorithm is more vulnerable to adversarial attacks, which the authors identify as problematic in real-world AR or VR systems.