GAURA: Generalizable Approach for Unified Restoration and Rendering of Arbitrary Views (2407.08221v1)

Abstract: Neural rendering methods can achieve near-photorealistic image synthesis of scenes from posed input images. However, when the images are imperfect, e.g., captured in very low-light conditions, state-of-the-art methods fail to reconstruct high-quality 3D scenes. Recent approaches have tried to address this limitation by modeling various degradation processes in the image formation model; however, this limits them to specific image degradations. In this paper, we propose a generalizable neural rendering method that can perform high-fidelity novel view synthesis under several degradations. Our method, GAURA, is learning-based and does not require any test-time scene-specific optimization. It is trained on a synthetic dataset that includes several degradation types. GAURA outperforms state-of-the-art methods on several benchmarks for low-light enhancement, dehazing, deraining, and on-par for motion deblurring. Further, our model can be efficiently fine-tuned to any new incoming degradation using minimal data. We thus demonstrate adaptation results on two unseen degradations, desnowing and removing defocus blur. Code and video results are available at vinayak-vg.github.io/GAURA.

• The paper introduces GAURA, a method that unifies image restoration and neural rendering for high-fidelity novel view synthesis from degraded images.
• It incorporates a degradation-aware latent module and an adaptive residual module to dynamically adjust restoration for various degradation types.
• Experimental evaluations demonstrate superior performance in low-light, motion blur, and dehazing tasks compared to existing state-of-the-art methods.

Generalizable Approach for Unified Restoration and Rendering of Arbitrary Views (GAURA)

The paper presents GAURA, a versatile neural rendering method aimed at addressing the challenge of high-fidelity novel view synthesis from degraded input images. This technique blends the strengths of generalizable neural radiance fields (NeRF) with advanced image restoration capabilities, making it applicable across various degradation types without requiring scene-specific optimization. GAURA was trained on a synthetic dataset mimicking diverse degradation types such as low-light conditions, haze, rain, and motion blur, showcasing impressive generalization across different scenarios.

Technical Contributions

The key technical innovations in GAURA can be summarized as follows:

1. Degradation-aware Latent Module (DLM): GAURA introduces learnable degradation-aware latent codes that encode degradation-specific information. These codes are utilized by the network to adjust its behavior dynamically according to the type of degradation present in the input images.
2. Adaptive Residual Module (ARM): To further improve the restoration quality, GAURA incorporates an adaptive residual feature derived from the input view closest to the target view to be rendered. This ensures that intra-class variability within each degradation type is captured effectively.
3. Integration with Generalizable NeRF: The method builds on the strengths of the Generalizable Neural Radiance Fields (e.g., GNT) by incorporating the DLM in the feature extraction, view transformers, and ray transformers. This integration enables the entire rendering process to be conditioned on the degradation type, effectively combining restoration and rendering into a unified framework.
4. Efficient Fine-tuning: GAURA supports efficient fine-tuning to new, unseen degradations with minimal data, making it practical for real-world applications. This is achieved through the modular design of the DLM, which allows easy expansion and adaptation to new imperfection types.

Experimental Evaluation

The paper provides extensive quantitative and qualitative evaluation across various tasks such as low-light enhancement, motion blur removal, image dehazing, and deraining. The results consistently demonstrate GAURA's superior performance compared to existing baselines including specialized NeRF extensions and state-of-the-art all-in-one single-image restoration methods followed by GNT for novel view synthesis.

Quantitative Results:

• Low-light Enhancement: GAURA achieves a PSNR of 19.91 and SSIM of 0.738, outperforming specialized methods which achieve 17.73 (PSNR) and 0.577 (SSIM) respectively.
• Motion Blur: GAURA demonstrates on-par performance with methods that require significant scene-specific optimization, showing remarkable PSNR of 22.12 and SSIM of 0.712.
• Image Dehazing: With a PSNR of 16.82 and SSIM of 0.759, GAURA proves to be efficient even for scenarios like haze removal which lack specialized 3D restoration techniques.

Qualitative Results:

GAURA consistently delivers clean and visually faithful renderings across diverse degradation types, effectively restoring fine details and maintaining high geometric accuracy. For instance, in the case of low-light conditions, GAURA demonstrates a superior ability to enhance scenes and recover finer structural details without introducing artifacts.

Implications and Future Work

The implications of GAURA are significant both practically and theoretically. By enabling high-fidelity novel view synthesis from degraded images, GAURA opens new possibilities for various applications including virtual reality, augmented reality, and digital film restoration. Practically, its capability to generalize across scenes and degradation types makes GAURA highly versatile, potentially reducing the need for multiple specialized models.

Theoretically, GAURA introduces an innovative paradigm for neural rendering by embedding degradation-specific priors directly into the neural network, bypassing the need for explicit physical modeling of the degradation process. This could inspire further research into more sophisticated latent space representations and adaptive neural architectures.

Conclusion

GAURA represents a significant advancement in the interplay of image restoration and neural rendering, achieving high-quality novel view synthesis from degraded images across various scenarios. The method's adaptability, efficiency, and generalization capabilities highlight its practical relevance and open up new avenues for future research. Future work could focus on fully automated degradation identification (blind restoration), extending the model's capability to handle multiple simultaneous degradations and exploring other neural architectures for even faster and more accurate rendering.