CoMoGaussian: Continuous Motion-Aware Gaussian Splatting from Motion-Blurred Images
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911339564236800 |
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| author | Lee, Jungho Kim, Donghyeong Lee, Dogyoon Cho, Suhwan Lee, Minhyeok Lee, Wonjoon Kim, Taeoh Wee, Dongyoon Lee, Sangyoun |
| author_facet | Lee, Jungho Kim, Donghyeong Lee, Dogyoon Cho, Suhwan Lee, Minhyeok Lee, Wonjoon Kim, Taeoh Wee, Dongyoon Lee, Sangyoun |
| contents | 3D Gaussian Splatting (3DGS) has gained significant attention due to its high-quality novel view rendering, motivating research to address real-world challenges. A critical issue is the camera motion blur caused by movement during exposure, which hinders accurate 3D scene reconstruction. In this study, we propose CoMoGaussian, a Continuous Motion-Aware Gaussian Splatting that reconstructs precise 3D scenes from motion-blurred images while maintaining real-time rendering speed. Considering the complex motion patterns inherent in real-world camera movements, we predict continuous camera trajectories using neural ordinary differential equations (ODEs). To ensure accurate modeling, we employ rigid body transformations, preserving the shape and size of the object but rely on the discrete integration of sampled frames. To better approximate the continuous nature of motion blur, we introduce a continuous motion refinement (CMR) transformation that refines rigid transformations by incorporating additional learnable parameters. By revisiting fundamental camera theory and leveraging advanced neural ODE techniques, we achieve precise modeling of continuous camera trajectories, leading to improved reconstruction accuracy. Extensive experiments demonstrate state-of-the-art performance both quantitatively and qualitatively on benchmark datasets, which include a wide range of motion blur scenarios, from moderate to extreme blur. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_05332 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CoMoGaussian: Continuous Motion-Aware Gaussian Splatting from Motion-Blurred Images Lee, Jungho Kim, Donghyeong Lee, Dogyoon Cho, Suhwan Lee, Minhyeok Lee, Wonjoon Kim, Taeoh Wee, Dongyoon Lee, Sangyoun Computer Vision and Pattern Recognition 3D Gaussian Splatting (3DGS) has gained significant attention due to its high-quality novel view rendering, motivating research to address real-world challenges. A critical issue is the camera motion blur caused by movement during exposure, which hinders accurate 3D scene reconstruction. In this study, we propose CoMoGaussian, a Continuous Motion-Aware Gaussian Splatting that reconstructs precise 3D scenes from motion-blurred images while maintaining real-time rendering speed. Considering the complex motion patterns inherent in real-world camera movements, we predict continuous camera trajectories using neural ordinary differential equations (ODEs). To ensure accurate modeling, we employ rigid body transformations, preserving the shape and size of the object but rely on the discrete integration of sampled frames. To better approximate the continuous nature of motion blur, we introduce a continuous motion refinement (CMR) transformation that refines rigid transformations by incorporating additional learnable parameters. By revisiting fundamental camera theory and leveraging advanced neural ODE techniques, we achieve precise modeling of continuous camera trajectories, leading to improved reconstruction accuracy. Extensive experiments demonstrate state-of-the-art performance both quantitatively and qualitatively on benchmark datasets, which include a wide range of motion blur scenarios, from moderate to extreme blur. |
| title | CoMoGaussian: Continuous Motion-Aware Gaussian Splatting from Motion-Blurred Images |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.05332 |