CRiM-GS: Continuous Rigid Motion-Aware Gaussian Splatting from Motion
  Blur Images
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.
668b8dedcc47c4aec57bc0eb
Junghe Lee
claimed_verified
2024-07-15T09:01:04.795Z
6692735b7948933f2af7b4f3
/avatars/90a2e8b1a5b15008a08941215fd48638.svg
Jungho Lee
user
Jho-Yonsei
668b8dedcc47c4aec57bc0ec
Donghyeong Kim
668b8dedcc47c4aec57bc0ed
Dogyoon Lee
668b8dedcc47c4aec57bc0ee
Suhwan Cho
claimed_verified
2024-07-09T08:32:43.339Z
668cf3cdd141a8c855411051
/avatars/e14e4c2e9e0ab87207dfaa590c3556bf.svg
Suhwan Cho
user
suhwan-cho
668b8dedcc47c4aec57bc0ef
Sangyoun Lee
admin_assigned
2024-07-09T08:09:40.485Z
6374fe62ce2759e3756f121d
/avatars/4009302c594c932a4300ab5d14b020fb.svg
SangYoun Lee
user
TakiMonev
CRiM-GS: Continuous Rigid Motion-Aware Gaussian Splatting from Motion
  Blur Images
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.
The study proposes CRiM-GS, a method that reconstructs accurate 3D scenes from blurry images using neural ODEs to model camera motion, achieving real-time rendering speed and state-of-the-art performance.
Neural radiance fields (NeRFs) camera motion blur continuous rigid motion-aware gaussian splatting (CRiM-GS) neural ordinary differential equations (ODEs) rigid body transformations SE(3) field continuous deformable 3D transformation