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.

;Comment: 19pages, 8 figures, conference