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:通过多视图图像和配对校准相机参数的新视角合成,NeRF的系统在严格控制的环境中展示了前一个基于NeRF的系统,很少注意不太理想的情景,包括暴露、照明变化和模糊等噪音的存在。特别是,尽管在现实情况下经常出现模糊现象,但能够处理模糊图像的NeRF却很少受到注意。对NeRF的模糊图像调查的少数研究没有考虑到3D空间的几何和外观一致性,而3D空间是3D重建的最重要因素之一。这导致构建场景的感知质量出现不一致和退化。因此,本文提出了一个DP-NERF,这是一个用于模糊图像的新颖的清洁NERF框架,受两个物理前期的限制。这些前文源自摄影机获取图像过程中的实际模糊过程。DP-NERF提出僵硬模糊的内核内核内核内核内核,利用物理前称和适应重力建议来改进当前图像质量的模糊性质量错误。我们用深层的深度和深层方向展示了当前GRF的图像的清晰度。