提升通用神经辐射场在模糊等退化下的重建鲁棒性
Towards Degradation-Robust Reconstruction in Generalizable NeRF
- 设计轻量模块，利用3D感知特征增强退化鲁棒性
- 在5万张不同模糊程度图像上验证，性能显著提升
- 适合关注真实场景3D重建鲁棒性的研究者
跨场景通用神经辐射场（GNeRF）通过源图像的深度特征表示场景，避免逐场景优化，具有实际应用潜力。然而，针对源图像中各类退化（如模糊）的鲁棒性研究仍不足，主要因缺乏大规模适配数据集。为此，我们构建了包含超过1000个场景、5万张图像的Objaverse Blur Dataset，涵盖多级模糊退化。同时设计了一种简单且模型无关的模块，通过轻量级深度估计器与去噪器提取3D感知特征，在多种主流GNeRF方法上均实现定量与视觉质量的提升，适用于不同退化类型与强度。相关数据集与代码将公开。
原文摘要 · Abstract (English)
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.
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