Neural Radiance Field (NeRF) has exhibited outstanding three-dimensional (3D) reconstruction quality via the novel view synthesis from multi-view images and paired calibrated camera parameters. However, previous NeRF-based systems have been demonstrated under strictly controlled settings, with little attention paid to less ideal scenarios, including with the presence of noise such as exposure, illumination changes, and blur. In particular, though blur frequently occurs in real situations, NeRF that can handle blurred images has received little attention. The few studies that have investigated NeRF for blurred images have not considered geometric and appearance consistency in 3D space, which is one of the most important factors in 3D reconstruction. This leads to inconsistency and the degradation of the perceptual quality of the constructed scene. Hence, this paper proposes a DP-NeRF, a novel clean NeRF framework for blurred images, which is constrained with two physical priors. These priors are derived from the actual blurring process during image acquisition by the camera. DP-NeRF proposes rigid blurring kernel to impose 3D consistency utilizing the physical priors and adaptive weight proposal to refine the color composition error in consideration of the relationship between depth and blur. We present extensive experimental results for synthetic and real scenes with two types of blur: camera motion blur and defocus blur. The results demonstrate that DP-NeRF successfully improves the perceptual quality of the constructed NeRF ensuring 3D geometric and appearance consistency. We further demonstrate the effectiveness of our model with comprehensive ablation analysis.

翻译：神经辐射场(NeRF)已通过多视角图像与配准相机参数的合成新视角展现出卓越的三维重建质量。然而，现有基于NeRF的系统均在严格受控条件下验证，鲜有关注曝光、光照变化及模糊等噪声影响下的非理想场景。特别地，尽管模糊在真实场景中频繁出现，能处理模糊图像的NeRF仍鲜有研究。少数针对模糊图像NeRF的工作未考虑三维空间中几何与外观一致性这一三维重建的核心要素，导致重建场景的不一致性与感知质量退化。为此，本文提出DP-NeRF——一种受两种物理先验约束的新型清晰NeRF框架。这些先验源自相机图像获取过程中的实际模糊机制。DP-NeRF通过物理先验引入刚性模糊核以强制三维一致性，并采用自适应权重提议机制优化考虑深度与模糊关系的色彩合成误差。我们针对相机运动模糊与散焦模糊两类场景，在合成数据和真实数据上开展了大量实验。结果表明，DP-NeRF成功提升了重建NeRF的感知质量，确保了三维几何与外观一致性。综合消融分析进一步验证了本模型的有效性。