3D-GP-LMVIC: Learning-based Multi-View Image Coding with 3D Gaussian Geometric Priors
<|reference_start|>3D-GP-LMVIC: Learning-based Multi-View Image Coding with 3D Gaussian Geometric Priors: Multi-view image compression is vital for 3D-related applications. To effectively model correlations between views, existing methods typically predict disparity between two views on a 2D plane, which works well for small disparities, such as in stereo images, but struggles with larger disparities caused by significant view changes. To address this, we propose a novel approach: learning-based multi-view image coding with 3D Gaussian geometric priors (3D-GP-LMVIC). Our method leverages 3D Gaussian Splatting to derive geometric priors of the 3D scene, enabling more accurate disparity estimation across views within the compression model. Additionally, we introduce a depth map compression model to reduce redundancy in geometric information between views. A multi-view sequence ordering method is also proposed to enhance correlations between adjacent views. Experimental results demonstrate that 3D-GP-LMVIC surpasses both traditional and learning-based methods in performance, while maintaining fast encoding and decoding speed.<|reference_end|>
@article{huang20243d-gp-lmvic:,
    title={3D-GP-LMVIC: Learning-based Multi-View Image Coding with 3D Gaussian
  Geometric Priors},
    author={Yujun Huang, Bin Chen, Niu Lian, Baoyi An, Shu-Tao Xia},
    journal={arXiv preprint arXiv:2409.04013},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.04013},
    primaryClass={cs.CV cs.IT cs.MM math.IT}
}