DiET-GS: Diffusion Prior and Event Stream-Assisted Motion Deblurring 3D
  Gaussian Splatting
Reconstructing sharp 3D representations from blurry multi-view images are
long-standing problem in computer vision. Recent works attempt to enhance
high-quality novel view synthesis from the motion blur by leveraging
event-based cameras, benefiting from high dynamic range and microsecond
temporal resolution. However, they often reach sub-optimal visual quality in
either restoring inaccurate color or losing fine-grained details. In this
paper, we present DiET-GS, a diffusion prior and event stream-assisted motion
deblurring 3DGS. Our framework effectively leverages both blur-free event
streams and diffusion prior in a two-stage training strategy. Specifically, we
introduce the novel framework to constraint 3DGS with event double integral,
achieving both accurate color and well-defined details. Additionally, we
propose a simple technique to leverage diffusion prior to further enhance the
edge details. Qualitative and quantitative results on both synthetic and
real-world data demonstrate that our DiET-GS is capable of producing
significantly better quality of novel views compared to the existing baselines.
Our project page is https://diet-gs.github.io
67ece4b12511c2aab09b84ca
Seungjun Lee
claimed_verified
2025-04-02T11:16:54.586Z
662f5b1e5aeedf55d209741a
https://cdn-avatars.huggingface.co/v1/production/uploads/662f5b1e5aeedf55d209741a/YdsbnRJPj_6PRfWbCNWfX.jpeg
Seungjun Lee
user
onandon
67ece4b12511c2aab09b84cb
Gim Hee Lee
admin_assigned
2025-04-02T12:29:18.073Z
64bc7568b7375f6b84503ce2
/avatars/d970c9990d9d84ac51ff225c1b26e304.svg
Gim Hee Lee
user
gimhee-lee
DiET-GS: Diffusion Prior and Event Stream-Assisted Motion Deblurring 3D
  Gaussian Splatting
Reconstructing sharp 3D representations from blurry multi-view images are
long-standing problem in computer vision. Recent works attempt to enhance
high-quality novel view synthesis from the motion blur by leveraging
event-based cameras, benefiting from high dynamic range and microsecond
temporal resolution. However, they often reach sub-optimal visual quality in
either restoring inaccurate color or losing fine-grained details. In this
paper, we present DiET-GS, a diffusion prior and event stream-assisted motion
deblurring 3DGS. Our framework effectively leverages both blur-free event
streams and diffusion prior in a two-stage training strategy. Specifically, we
introduce the novel framework to constraint 3DGS with event double integral,
achieving both accurate color and well-defined details. Additionally, we
propose a simple technique to leverage diffusion prior to further enhance the
edge details. Qualitative and quantitative results on both synthetic and
real-world data demonstrate that our DiET-GS is capable of producing
significantly better quality of novel views compared to the existing baselines.
Our project page is https://diet-gs.github.io
DiET-GS, a diffusion prior and event stream-assisted framework, improves 3D representation reconstruction from motion-blurred images by enhancing color accuracy and detail preservation.
diffusion prior event stream motion deblurring 3DGS event double integral edge details