  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.
