RustNeRF: Robust Neural Radiance Field with Low-Quality Images

• URL: http://arxiv.org/abs/2401.03257v1
• Date: Sat, 6 Jan 2024 16:54:02 GMT
• Title: RustNeRF: Robust Neural Radiance Field with Low-Quality Images
• Authors: Mengfei Li, Ming Lu, Xiaofang Li, Shanghang Zhang
• Abstract summary: We present RustNeRF for real-world high-quality Neural Radiance Fields (NeRF) To improve NeRF's robustness under real-world inputs, we train a 3D-aware preprocessing network that incorporates real-world degradation modeling. We propose a novel implicit multi-view guidance to address information loss during image degradation and restoration.
• Score: 29.289408956815727
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent work on Neural Radiance Fields (NeRF) exploits multi-view 3D consistency, achieving impressive results in 3D scene modeling and high-fidelity novel-view synthesis. However, there are limitations. First, existing methods assume enough high-quality images are available for training the NeRF model, ignoring real-world image degradation. Second, previous methods struggle with ambiguity in the training set due to unmodeled inconsistencies among different views. In this work, we present RustNeRF for real-world high-quality NeRF. To improve NeRF's robustness under real-world inputs, we train a 3D-aware preprocessing network that incorporates real-world degradation modeling. We propose a novel implicit multi-view guidance to address information loss during image degradation and restoration. Extensive experiments demonstrate RustNeRF's advantages over existing approaches under real-world degradation. The code will be released.

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