Détail du document
Identifiant
oai:arXiv.org:2409.04013
Sujet
Computer Science - Computer Vision... Computer Science - Information The... Computer Science - MultimediaAuteur
Huang, Yujun Chen, Bin Lian, Niu An, Baoyi Xia, Shu-TaoCatégorie
Computer Science
Année
2024
Date de référencement
11/09/2024
Mots clés
science image views computerRésumé
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
Huang, Yujun,Chen, Bin,Lian, Niu,An, Baoyi,Xia, Shu-Tao, 2024, 3D-GP-LMVIC: Learning-based Multi-View Image Coding with 3D Gaussian Geometric Priors
Projection-Specific Heterogeneity of the Axon Initial Segment of Pyramidal Neurons in the Prelimbic Cortex
heterogeneity projection-specific
