3D-LMVIC: Learning-based Multi-View Image Coding with 3D Gaussian Geometric Priors
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917959301070848 |
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| author | Huang, Yujun Chen, Bin Lian, Niu An, Baoyi Xia, Shu-Tao |
| author_facet | Huang, Yujun Chen, Bin Lian, Niu An, Baoyi Xia, Shu-Tao |
| contents | Existing multi-view image compression methods often rely on 2D projection-based similarities between views to estimate disparities. While effective for small disparities, such as those in stereo images, these methods struggle with the more complex disparities encountered in wide-baseline multi-camera systems, commonly found in virtual reality and autonomous driving applications. To address this limitation, we propose 3D-LMVIC, a novel learning-based multi-view image compression framework that leverages 3D Gaussian Splatting to derive geometric priors for accurate disparity estimation. Furthermore, we introduce a depth map compression model to minimize geometric redundancy across views, along with a multi-view sequence ordering strategy based on a defined distance measure between views to enhance correlations between adjacent views. Experimental results demonstrate that 3D-LMVIC achieves superior performance compared to both traditional and learning-based methods. Additionally, it significantly improves disparity estimation accuracy over existing two-view approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_04013 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | 3D-LMVIC: Learning-based Multi-View Image Coding with 3D Gaussian Geometric Priors Huang, Yujun Chen, Bin Lian, Niu An, Baoyi Xia, Shu-Tao Computer Vision and Pattern Recognition Information Theory Multimedia Existing multi-view image compression methods often rely on 2D projection-based similarities between views to estimate disparities. While effective for small disparities, such as those in stereo images, these methods struggle with the more complex disparities encountered in wide-baseline multi-camera systems, commonly found in virtual reality and autonomous driving applications. To address this limitation, we propose 3D-LMVIC, a novel learning-based multi-view image compression framework that leverages 3D Gaussian Splatting to derive geometric priors for accurate disparity estimation. Furthermore, we introduce a depth map compression model to minimize geometric redundancy across views, along with a multi-view sequence ordering strategy based on a defined distance measure between views to enhance correlations between adjacent views. Experimental results demonstrate that 3D-LMVIC achieves superior performance compared to both traditional and learning-based methods. Additionally, it significantly improves disparity estimation accuracy over existing two-view approaches. |
| title | 3D-LMVIC: Learning-based Multi-View Image Coding with 3D Gaussian Geometric Priors |
| topic | Computer Vision and Pattern Recognition Information Theory Multimedia |
| url | https://arxiv.org/abs/2409.04013 |