Towards Degradation-Robust Reconstruction in Generalizable NeRF (2411.11691v1)

Abstract: Generalizable Neural Radiance Field (GNeRF) across scenes has been proven to be an effective way to avoid per-scene optimization by representing a scene with deep image features of source images. However, despite its potential for real-world applications, there has been limited research on the robustness of GNeRFs to different types of degradation present in the source images. The lack of such research is primarily attributed to the absence of a large-scale dataset fit for training a degradation-robust generalizable NeRF model. To address this gap and facilitate investigations into the degradation robustness of 3D reconstruction tasks, we construct the Objaverse Blur Dataset, comprising 50,000 images from over 1000 settings featuring multiple levels of blur degradation. In addition, we design a simple and model-agnostic module for enhancing the degradation robustness of GNeRFs. Specifically, by extracting 3D-aware features through a lightweight depth estimator and denoiser, the proposed module shows improvement on different popular methods in GNeRFs in terms of both quantitative and visual quality over varying degradation types and levels. Our dataset and code will be made publicly available.

• The paper presents a novel Objaverse Blur Dataset with over 50K images to train robust GNeRF models under blur degradations.
• It introduces a 3D-aware feature extraction module that aligns and restores features to enhance degradation robustness.
• Extensive experiments show significant improvement in PSNR and rendering accuracy, broadening applications in real-world conditions.

Towards Degradation-Robust Reconstruction in Generalizable NeRF

The paper "Towards Degradation-Robust Reconstruction in Generalizable NeRF" addresses the challenges of degrading image quality in real-world applications of Generative Neural Radiance Fields (GNeRF). The central issue highlighted by the authors pertains to GNeRF's susceptibility to various image degradations, such as blur and noise, which traditionally hamper the model's ability to generalize across different scenes. To combat these challenges, the paper introduces a novel dataset termed the Objaverse Blur Dataset, alongside a lightweight module that enhances GNeRF's performance under degraded conditions.

Key Contributions

1. Objaverse Blur Dataset: One of the pivotal contributions of this paper is the construction of the Objaverse Blur Dataset—a large-scale dataset containing over 50,000 images across 1000 distinct settings, with varying levels of blur degradation. This dataset fills a significant gap by providing an extensive resource for training 3D reconstruction models to be robust against blur degradations. The synthetic blur levels are generated with high 3D consistency, simulating real-world camera motion during image capture.
2. 3D-Aware Feature Extraction Module: A critical technical contribution of the paper is the introduction of a model-agnostic 3D-aware feature extraction plugin, designed to enhance the degradation robustness of GNeRFs. This module operates through a two-step process:
   • A self-supervised depth estimator aligns input images across views to enhance feature alignment.
   • A 3D-aware restoration head processes these features, promoting invariance to degradation and improving the overall image features used in rendering.
3. Methodology and Results: The methods proposed are evaluated through comprehensive experiments across multiple GNeRF frameworks. Noteworthy quantitative results showcase significant improvements in rendering accuracy under various levels of blur and noise. For instance, experiments with the Objaverse Blur Dataset revealed improvements of up to 0.92 in PSNR for particular blur levels.
4. Versatility and Impact: The module designed by the authors exhibits versatility, allowing integration with a variety of GNeRF models without significant computational overhead. Only minimal adjustments in inference speed were noted compared to baseline models, indicating the module’s viability for diverse applications without compromising efficiency. Additionally, the robustness enhancement extends even to adversarial image perturbations, further broadening its practical relevance.

Implications and Speculations

The implications of this research are manifold. Practically, the improvement in degradation robustness makes GNeRFs more applicable in real-world conditions, such as drone footage and autonomous driving, where image quality can be unpredictable. Theorectically, the paper opens avenues for exploring generalized neural field models that can adapt to an array of environmental and situational variabilities.

Future developments might explore extending these techniques to other forms of degradation, possibly integrating inverse physical models to better understand scene conditions. Moreover, improvements in hardware advancements could facilitate more computationally intensive methods that further contribute to realizing real-time, degradation-robust neural rendering.

In conclusion, the paper sets a foundational step towards achieving robust and generalizable 3D reconstructions in the face of common imaging adversities. As the field progresses, building upon this work will be crucial for advancing the capabilities and reliability of neural radiance fields across varying real-world applications.