CRiM-GS: Continuous Rigid Motion-Aware Gaussian Splatting from Motion
  Blur Images
Authors: name: Junghe Lee, name: Donghyeong Kim, name: Dogyoon Lee, name: Suhwan Cho, name: Sangyoun Lee

Neural radiance fields (NeRFs) have received significant attention due to
their high-quality novel view rendering ability, prompting research to address
various real-world cases. One critical challenge is the camera motion blur
caused by camera movement during exposure time, which prevents accurate 3D
scene reconstruction. In this study, we propose continuous rigid motion-aware
gaussian splatting (CRiM-GS) to reconstruct accurate 3D scene from blurry
images with real-time rendering speed. Considering the actual camera motion
blurring process, which consists of complex motion patterns, we predict the
continuous movement of the camera based on neural ordinary differential
equations (ODEs). Specifically, we leverage rigid body transformations to model
the camera motion with proper regularization, preserving the shape and size of
the object. Furthermore, we introduce a continuous deformable 3D transformation
in the SE(3) field to adapt the rigid body transformation to
real-world problems by ensuring a higher degree of freedom. By revisiting
fundamental camera theory and employing advanced neural network training
techniques, we achieve accurate modeling of continuous camera trajectories. We
conduct extensive experiments, demonstrating state-of-the-art performance both
quantitatively and qualitatively on benchmark datasets.