Summary of Crim-gs: Continuous Rigid Motion-aware Gaussian Splatting From Motion-blurred Images, by Jungho Lee et al.
CRiM-GS: Continuous Rigid Motion-Aware Gaussian Splatting from Motion-Blurred Images
by Jungho Lee, Donghyeong Kim, Dogyoon Lee, Suhwan Cho, Minhyeok Lee, Sangyoun Lee
First submitted to arxiv on: 4 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study proposes CRiM-GS, a novel method for reconstructing 3D scenes from motion-blurred images while maintaining real-time rendering speed. The researchers predict continuous camera trajectories using neural ordinary differential equations (ODE) to handle complex motion patterns. They also introduce an adaptive distortion-aware transformation to compensate for nonlinear distortions and unpredictable camera movements. This approach achieves state-of-the-art performance on benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps us create better 3D pictures from blurry images by figuring out where the camera was moving during exposure. The researchers developed a new way to predict how the camera moved using special math equations and computer algorithms. They also found ways to fix problems that happen when cameras move in weird ways, like rolling shutter effects. Their method works really well on test datasets. |
