Neural Radiance Field (NeRF) has gained considerable attention recently for
3D scene reconstruction and novel view synthesis due to its remarkable
synthesis quality. However, image blurriness caused by defocus or motion, which
often occurs when capturing scenes in the wild, significantly degrades its
reconstruction quality. To address this problem, We propose Deblur-NeRF, the
first method that can recover a sharp NeRF from blurry input. We adopt an
analysis-by-synthesis approach that reconstructs blurry views by simulating the
blurring process, thus making NeRF robust to blurry inputs. The core of this
simulation is a novel Deformable Sparse Kernel (DSK) module that models
spatially-varying blur kernels by deforming a canonical sparse kernel at each
spatial location. The ray origin of each kernel point is jointly optimized,
inspired by the physical blurring process. This module is parameterized as an
MLP that has the ability to be generalized to various blur types. Jointly
optimizing the NeRF and the DSK module allows us to restore a sharp NeRF. We
demonstrate that our method can be used on both camera motion blur and defocus
blur: the two most common types of blur in real scenes. Evaluation results on
both synthetic and real-world data show that our method outperforms several
baselines. The synthetic and real datasets along with the source code is
publicly available at this https URL

提出了 Deblur-NeRF 方法，采用分析合成方法重建模糊视图，通过 Deformable Sparse Kernel 模块对空间可变模糊核进行建模，共同优化 NeRF 和 DSK 模块，从而恢复锐利的 NeRF。