  Recent studies in Radiance Fields have paved the robust way for novel view
synthesis with their photorealistic rendering quality. Nevertheless, they
usually employ neural networks and volumetric rendering, which are costly to
train and impede their broad use in various real-time applications due to the
lengthy rendering time. Lately 3D Gaussians splatting-based approach has been
proposed to model the 3D scene, and it achieves remarkable visual quality while
rendering the images in real-time. However, it suffers from severe degradation
in the rendering quality if the training images are blurry. Blurriness commonly
occurs due to the lens defocusing, object motion, and camera shake, and it
inevitably intervenes in clean image acquisition. Several previous studies have
attempted to render clean and sharp images from blurry input images using
neural fields. The majority of those works, however, are designed only for
volumetric rendering-based neural radiance fields and are not straightforwardly
applicable to rasterization-based 3D Gaussian splatting methods. Thus, we
propose a novel real-time deblurring framework, Deblurring 3D Gaussian
Splatting, using a small Multi-Layer Perceptron (MLP) that manipulates the
covariance of each 3D Gaussian to model the scene blurriness. While Deblurring
3D Gaussian Splatting can still enjoy real-time rendering, it can reconstruct
fine and sharp details from blurry images. A variety of experiments have been
conducted on the benchmark, and the results have revealed the effectiveness of
our approach for deblurring. Qualitative results are available at
https://benhenryl.github.io/Deblurring-3D-Gaussian-Splatting/
