ExBluRF: Efficient Radiance Fields for Extreme Motion Blurred Images
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866917596570320896 |
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| author | Lee, Dongwoo Oh, Jeongtaek Rim, Jaesung Cho, Sunghyun Lee, Kyoung Mu |
| author_facet | Lee, Dongwoo Oh, Jeongtaek Rim, Jaesung Cho, Sunghyun Lee, Kyoung Mu |
| contents | We present ExBluRF, a novel view synthesis method for extreme motion blurred images based on efficient radiance fields optimization. Our approach consists of two main components: 6-DOF camera trajectory-based motion blur formulation and voxel-based radiance fields. From extremely blurred images, we optimize the sharp radiance fields by jointly estimating the camera trajectories that generate the blurry images. In training, multiple rays along the camera trajectory are accumulated to reconstruct single blurry color, which is equivalent to the physical motion blur operation. We minimize the photo-consistency loss on blurred image space and obtain the sharp radiance fields with camera trajectories that explain the blur of all images. The joint optimization on the blurred image space demands painfully increasing computation and resources proportional to the blur size. Our method solves this problem by replacing the MLP-based framework to low-dimensional 6-DOF camera poses and voxel-based radiance fields. Compared with the existing works, our approach restores much sharper 3D scenes from challenging motion blurred views with the order of 10 times less training time and GPU memory consumption. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_08957 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | ExBluRF: Efficient Radiance Fields for Extreme Motion Blurred Images Lee, Dongwoo Oh, Jeongtaek Rim, Jaesung Cho, Sunghyun Lee, Kyoung Mu Computer Vision and Pattern Recognition We present ExBluRF, a novel view synthesis method for extreme motion blurred images based on efficient radiance fields optimization. Our approach consists of two main components: 6-DOF camera trajectory-based motion blur formulation and voxel-based radiance fields. From extremely blurred images, we optimize the sharp radiance fields by jointly estimating the camera trajectories that generate the blurry images. In training, multiple rays along the camera trajectory are accumulated to reconstruct single blurry color, which is equivalent to the physical motion blur operation. We minimize the photo-consistency loss on blurred image space and obtain the sharp radiance fields with camera trajectories that explain the blur of all images. The joint optimization on the blurred image space demands painfully increasing computation and resources proportional to the blur size. Our method solves this problem by replacing the MLP-based framework to low-dimensional 6-DOF camera poses and voxel-based radiance fields. Compared with the existing works, our approach restores much sharper 3D scenes from challenging motion blurred views with the order of 10 times less training time and GPU memory consumption. |
| title | ExBluRF: Efficient Radiance Fields for Extreme Motion Blurred Images |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2309.08957 |