Source-linked AI summary
Generalizable Patch-Based Neural Rendering
Mohammed Suhail, Carlos Esteves, Leonid Sigal, Ameesh Makadia
TL;DR
Generalizable novel-view synthesis remains difficult because many neural rendering methods overfit individual scenes or rely on scene-dependent features and volume rendering. The paper predicts target-ray colors from epipolar patches using transformers and canonicalized ray representations, outperforming prior baselines while using substantially less training data. Its main scope limitation is that small-patch processing requires many reference views for meaningful features.
Problem
Existing neural rendering methods often overfit single scenes, while generalizable approaches rely on deep convolutional features and scene-specific inputs that transfer poorly to unseen scenes.
Method
The model extracts local patches along reference-view epipolar lines, processes them with stacked transformers, and uses canonicalized ray, depth, and camera encodings to predict target-ray colors directly.
Results
The method outperforms previous baselines across multiple train and evaluation datasets while using as little as 11% of the training data used by prior work.
Takeaways & Limitations
Patch-based transformers and reference-frame-independent representations support novel-view synthesis of unseen scenes without deep convolutional features or NeRF-like volume rendering.
Takeaways & Limitations
Because it operates on small local patches, the model relies on a large number of reference views to produce meaningful features.
Abstract
from arXiv · showhide
Neural rendering has received tremendous attention since the advent of Neural Radiance Fields (NeRF), and has pushed the state-of-the-art on novel-view synthesis considerably. The recent focus has been on models that overfit to a single scene, and the few attempts to learn models that can synthesize novel views of unseen scenes mostly consist of combining deep convolutional features with a NeRF-like model. We propose a different paradigm, where no deep features and no NeRF-like volume rendering are needed. Our method is capable of predicting the color of a target ray in a novel scene directly, just from a collection of patches sampled from the scene. We first leverage epipolar geometry to extract patches along the epipolar lines of each reference view. Each patch is linearly projected into a 1D feature vector and a sequence of transformers process the collection. For positional encoding, we parameterize rays as in a light field representation, with the crucial difference that the coordinates are canonicalized with respect to the target ray, which makes our method independent of the reference frame and improves generalization. We show that our approach outperforms the state-of-the-art on novel view synthesis of unseen scenes even when being trained with considerably less data than prior work.
1 Introduction
The paper targets generalizable novel-view synthesis for unseen scenes, avoiding scene-specific retraining and replacing deep convolutional features and NeRF-like volume rendering with patch-based transformers and canonicalized representations.
- Scene-specific neural rendering methods produce high-quality views but require retraining for each new scene, limiting practical application.
- The proposed model predicts target-ray colors in unseen scenes directly from local patches sampled along reference-view epipolar lines.It uses patches rather than deep convolutional features and does not require NeRF-like volume rendering.
- Stacked transformers process different subsets of the patch collection to learn, combine, and aggregate visual information across views, depths, and reference cameras.The architecture follows the patch collection’s structure, refining representations along epipolar lines and reference views.
- Canonicalized positional encodings for rays, depths, and camera poses make the inputs independent of the scene’s frame of reference and improve generalization to unseen scenes.
- The model outperforms previous baselines across multiple training and evaluation datasets while using as little as 11% of prior training data in certain cases.
2 Related Work
Prior generalizable neural-rendering methods combine deep features with NeRF-like rendering, whereas this work uses local patches, transformers, and direct color prediction without structured scene representations or volume rendering.
- The method directly predicts target pixel colors from reference patches, avoiding both structured neural scene representations and volume rendering.
- The model can be trained once across scenes and applied to novel scenes, avoiding the scene-specific retraining common in prior neural-rendering methods.
- 2.2 Image-based rendering: Image-based rendering commonly synthesizes novel views by warping and compositing reference images, with prior methods differing in their use of geometry.
- Unlike prior approaches using convolutional features and NeRF-like rendering, the method operates on linear projections of local patches and produces final colors directly.
- Canonicalized ray, point, and camera representations make the method independent of the input frame of reference and improve generalization ability.
- 2.3 Transformers in vision: Transformers are used throughout the pipeline, from patch embedding to target-ray color prediction, with three transformers collapsing different dimensions.
3 Approach
The approach learns a generic renderer for unseen scenes from local patches and reference-camera geometry, using transformers, light-field ray encodings, and canonicalization. It aggregates patch information across views and epipolar samples, then directly blends reference colors to predict the target ray.
- 3 Approach: The model targets novel-view synthesis of unseen scenes using a reference-frame-agnostic rendering network built from local patches observed by nearby cameras.It maps a target light field and reference patches to radiance without training on the target scene.
- 3 Approach: The three-stage transformer processes view sequences, depth sequences, and aggregated view features to build a multiview representation for color prediction.The first stage operates over K views for each depth, while the second collapses M depth samples for each view before the third aggregates views.
- 3 Approach: Reference images are selected from nearby views, and patches are sampled along the epipolar line corresponding to the target pixel.This uses epipolar geometry to isolate image regions relevant to the target ray rather than partitioning whole images uniformly.
- 3 Approach: Patch inputs are flattened, linearly projected into embeddings, and augmented with ray, depth, and relative-camera positional information.The geometric encodings preserve patch location, epipolar-sample distance, and target-to-reference camera relationships across changing batches.
- 3.5 Rendering network: Visual features from multiple epipolar lines are combined by a transformer to establish correspondences, and attention weights directly blend reference pixel colors.The final prediction avoids NeRF-like volume rendering and learned-feature color projection.
4 Experiments
Experiments evaluate generalization to unseen scenes under two training settings and ablate the main components. The method outperforms baselines across datasets despite using substantially less training data in setting 1.
- Implementation: Training uses three eight-block transformers, K = 10 reference views, N = 20 candidate views, and 250k optimization iterations.The model is trained with batches of 4096 rays and takes approximately 24 hours on 32 TPUs.
- Setting 1: Our method outperforms baselines on most metrics in setting 1 despite training on only 11% of IBRNet’s training data.It is evaluated on real forward-facing, Shiny, and Blender datasets; IBRNet and GeoNeRF use larger training sets, and GeoNeRF also uses depth maps.
- Setting 2: On DTU and Blender, our model consistently outperforms MVSNeRF across all three metrics when comparing each method’s best reference-view count.Our method performs best with 10 views, whereas MVSNeRF uses 3; evaluating MVSNeRF with 10 views did not improve its performance.
- Setting 1: Shiny is more challenging than LLFF because its scenes differ in scale and view density, while IBRNet exhibits artifacts absent from our renderings.
- Ablations: The ablation study tests models trained on LLFF and IBRNet scenes and evaluated on real forward-facing scenes at 504 × 378 resolution.The base model omits the visual feature transformer and coordinate canonicalization, and the study incrementally adds the proposed components.
5 Limitations
The method’s local-patch design improves generalization but creates practical constraints. It requires many reference views and has slow training, although rendering is fast.
- Because it relies on small local patches, the model needs a large number of views to produce meaningful features.Against MVSNeRF, it uses 10 reference views versus 3, despite achieving higher accuracy.
- On DTU and Blender, the method outperforms baselines, but its stronger accuracy comes with a higher reference-view requirement than MVSNeRF.
- Ablations evaluate the visual feature transformer and canonicalized positional encoding as the main contributors to generalization performance.
- Rendering is fast because the method consists only of forward steps, but training is slow and comparable with LFNR.
6 Conclusion
The paper concludes that patch-based transformers can generate novel views of unseen scenes without deep visual features or NeRF-like volume rendering. The method achieves state-of-the-art performance while using only 11% of the training data.
- The method predicts arbitrary-ray colors from epipolar-constrained local patches and uses a three-stage transformer with canonicalized positional encodings.
- It outperforms the current state of the art while using only 11% of the training data.
- The appendix provides additional ablations, timing evaluations, train/evaluation combinations, and qualitative results.
Generalizable Patch-Based Neural Rendering Supplementary Material
The supplementary material accompanies the paper with author and publication metadata. It identifies the authors, affiliations, and arXiv version.
- The paper is authored by Mohammed Suhail, Carlos Esteves, Leonid Sigal, and Ameesh Makadia.
- The authors are affiliated with the University of British Columbia, Vector Institute for AI, Canada CIFAR AI Chair, and Google.
- The document is arXiv version 2, dated 28 July 2022.
A.1 Fine-tuning
Fine-tuning experiments evaluate the model on real-forward-facing scenes, while varying reference-view count tests how additional views affect synthesis.
- A.1 Fine-tuning: Fine-tuning on RFF setting 1 evaluates average metrics across scenes after 10k iterations per test scene.The protocol follows IBRNet and fine-tunes the model separately on each RFF test scene.
- A.1 Fine-tuning: The approach improves over baselines on unseen scenes both without retraining and after a few fine-tuning iterations.
- A.1 Fine-tuning: Using up to 10 reference images benefits view synthesis on the real-forward-facing dataset.Variants use 3, 5, 7, 10, and 12 reference images and are trained on LLFF and IBRNet forward-facing scenes.
A.3 RGB Prediction
The model predicts target-ray colors by linearly combining reference-image pixel colors rather than aggregated visual features. This pixel-combination design generalizes better in the reported comparisons, with complementary efficiency and benchmark trade-offs.
- A.3 RGB Prediction: The model predicts weights for a linear combination of reference-image pixel colors to synthesize a novel view.The alternative predicts output color from aggregated features followed by a learned mapping.
- A.3 RGB Prediction: Combining reference-view colors outperforms combining visual features for view synthesis.Table A.3 reports the average comparison between feature interpolation and color interpolation.
- A.3 RGB Prediction: On RFF scenes from a model trained only on DTU, MVSNeRF achieves better PSNR and LPIPS, while this method achieves better average SSIM.The comparison is reported across all RFF scenes.
- A.3 RGB Prediction: Training runs at around 3.2 steps per second on 32 TPUs, while rendering one image takes around 15 seconds.LFNR is slightly faster at 4.2 steps per second and renders an image in around 10 seconds on the same hardware.
B Quatlitative Results
Qualitative DTU comparisons show that the proposed model produces sharper visual details than MVSNeRF. The evaluation focuses on rendered boundaries and textures.
- B Quatlitative Results: Compared with MVSNeRF, the model produces renderings with sharper boundaries and textures on the DTU test set.
- B Quatlitative Results: The qualitative comparison is made against MVSNeRF using DTU test-set renderings.
- B Quatlitative Results: Figure B.1 presents qualitative results on DTU.