Rock Solid Texture Synthesis Based on 3D-CA-GAN
Duan Lian
Feng Yun
Hua Weihua
Chen Qihao
Liu Xiuguo
Zhang Kun
Fu Wei
Abstract：Solid texture synthesis based on 2D samples(deep learning)is an important pathway for rock solid texture generation,which currently suffers from the inability of long distance dependence and color distortion.In this paper,it proposes an innovative method based on 3D coordinate attention generative adversarial network(3D-CA-GAN).By extending the coordinate attention mechanism to three-dimensional space(3D-CA)and combining the content-aware upsampling module and multi-scale discriminator,high-fidelity modeling of the spatial distribution of mineral particles is achieved.Experiments show that the method significantly outperforms existing techniques in terms of SSIM(0.773),PSNR(24.92%enhancement),and LPIPS(0.110 reduction),and ablation experiments further validate that the 3D-CA module improves the SSIM of directional textures by 14.69%.This study provides a new solution to texture synthesis with realism for geological modeling,and its 3D attention framework is useful for generic generation tasks.
Keywords：rockssolid texturehybrid dilated convolutionattention module3D-CA-GAN3d-modeling
Publication Date：2025-11-30
Online Publishing Date：2025-12-15(First online date of this platform, not the publication date of the document)
Pages：15( 4499-4513 )