title: CRiM-GS: Continuous Rigid Motion-Aware Gaussian Splatting from Motion
    Blur Images
summary: 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.
paper_authors._id: 668b8dedcc47c4aec57bc0eb
paper_authors.name: Junghe Lee
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-15T09:01:04.795Z
paper_authors._id: 6692735b7948933f2af7b4f3
paper_authors.avatarUrl: /avatars/90a2e8b1a5b15008a08941215fd48638.svg
paper_authors.fullname: Jungho Lee
paper_authors.type: user
paper_authors.user: Jho-Yonsei
paper_authors._id: 668b8dedcc47c4aec57bc0ec
paper_authors.name: Donghyeong Kim
paper_authors._id: 668b8dedcc47c4aec57bc0ed
paper_authors.name: Dogyoon Lee
paper_authors._id: 668b8dedcc47c4aec57bc0ee
paper_authors.name: Suhwan Cho
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-09T08:32:43.339Z
paper_authors._id: 668cf3cdd141a8c855411051
paper_authors.avatarUrl: /avatars/e14e4c2e9e0ab87207dfaa590c3556bf.svg
paper_authors.fullname: Suhwan Cho
paper_authors.type: user
paper_authors.user: suhwan-cho
paper_authors._id: 668b8dedcc47c4aec57bc0ef
paper_authors.name: Sangyoun Lee
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-09T08:09:40.485Z
paper_authors._id: 6374fe62ce2759e3756f121d
paper_authors.avatarUrl: /avatars/4009302c594c932a4300ab5d14b020fb.svg
paper_authors.fullname: SangYoun Lee
paper_authors.type: user
paper_authors.user: TakiMonev
paper_title: CRiM-GS: Continuous Rigid Motion-Aware Gaussian Splatting from Motion
    Blur Images
paper_summary: 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.
paper_ai_keywords: Neural radiance fields (NeRFs) novel than continuous rigid motion-aware gaussian splatting (CRiM-GS) motion-aware gaussian splatting camera motion blur 3D scene reconstruction continuous movement neural ordinary differential equations (ODEs) rigid body transformations regularization continuous deformable 3D transformation SE(3) field camera trajectories state-of-the-art performance