基于3D-CA-GAN的岩石体纹理合成技术

段炼; 冯云; 花卫华; 陈启浩; 刘修国; 张坤; 付伟

doi:10.3799/dqkx.2025.134

Volume 50 Issue 11

Nov. 2025

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Duan Lian, Feng Yun, Hua Weihua, Chen Qihao, Liu Xiuguo, Zhang Kun, Fu Wei, 2025. Rock Solid Texture Synthesis Based on 3D-CA-GAN. Earth Science, 50(11): 4499-4513. doi: 10.3799/dqkx.2025.134

Citation:

Duan Lian, Feng Yun, Hua Weihua, Chen Qihao, Liu Xiuguo, Zhang Kun, Fu Wei, 2025. Rock Solid Texture Synthesis Based on 3D-CA-GAN. Earth Science, 50(11): 4499-4513. doi: 10.3799/dqkx.2025.134

Duan Lian, Feng Yun, Hua Weihua, Chen Qihao, Liu Xiuguo, Zhang Kun, Fu Wei, 2025. Rock Solid Texture Synthesis Based on 3D-CA-GAN. Earth Science, 50(11): 4499-4513. doi: 10.3799/dqkx.2025.134

Citation:

Duan Lian, Feng Yun, Hua Weihua, Chen Qihao, Liu Xiuguo, Zhang Kun, Fu Wei, 2025. Rock Solid Texture Synthesis Based on 3D-CA-GAN. Earth Science, 50(11): 4499-4513. doi: 10.3799/dqkx.2025.134

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Rock Solid Texture Synthesis Based on 3D-CA-GAN

doi: 10.3799/dqkx.2025.134

Duan Lian^{1
,
,},
Feng Yun¹,
Hua Weihua^{1
,
,
,},
Chen Qihao¹,
Liu Xiuguo¹,
Zhang Kun²,
Fu Wei²

1.
School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430078, China
2.
China Railway First Survey and Design Institute Group Co. Ltd., Xi'an 710043

Received Date: 2025-04-15
Publish Date: 2025-11-25

Abstract

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.
- rocks,
- solid texture,
- hybrid dilated convolution,
- attention module,
- 3D-CA-GAN,
- 3d-modeling

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References(29)

References

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