CT2BSE: 3D BSE Microstructural Image Cross-Device Generation from µCT for Cement Hydration via Voxel Swin Transformer
Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence. Main Track. Pages 1106-1114.
https://doi.org/10.24963/ijcai.2026/124
Keywords
- Computer Vision: Computational photography
- Computer Vision: Image and video synthesis and generation
- Computer Vision: 3D computer vision
Abstract
Acquiring three-dimensional(3D) microstructural images of cement hydration reveals critical microscale features essential for understanding hydration mechanisms and advancing material development. Micro-computed tomography(µCT) is widely used to capture these images due to its non-destructive, repeatable 3D imaging capability, despite at high operational cost. However, µCT suffers from limited resolution, weak texture, and missing phase information. In contrast, backscattered electron(BSE) provides high-resolution, phase-sensitive, and texture-rich images but are confined to 2D form. Inspired by the idea of fusing BSE style into µCT images for computationally imaging 3D BSE data, this paper proposes a cross-device generation method, termed CT2BSE, to construct 3D BSE microstructural images from µCT for cement hydration. A Voxel Swin Transformer is proposed to extract 3D μCT features by improving Video Swin Transformer with a Content-Aware Positional Encoding–3D, making encoder better suited for homogeneous images such as cement. Furthermore, a Holistic Style Injector–3D is proposed to fuse 3D μCT features with 2D BSE characteristics, thereby mitigating feature instability caused by the extreme intensity variations typically exhibited in cement images. Subsequently, the fused features are decoded into 3D volumes aligned with the BSE style while preserving original microstructure. Experimental results demonstrate that CT2BSE can generate high-fidelity 3D BSE images from μCT.
@inproceedings{ijcai2026p124,
title = {CT2BSE: 3D BSE Microstructural Image Cross-Device Generation from µCT for Cement Hydration via Voxel Swin Transformer},
author = {Gao, Haozhong and Zhang, Liangliang and Han, Yamin and Han, Ruiqi and Wang, Lin and Yang, Bo},
booktitle = {Proceedings of the Thirty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-26}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Diego Calvanese},
pages = {1106--1114},
year = {2026},
month = {8},
note = {Main Track},
doi = {10.24963/ijcai.2026/124},
url = {https://doi.org/10.24963/ijcai.2026/124},
}
TY - CPAPER TI - CT2BSE: 3D BSE Microstructural Image Cross-Device Generation from µCT for Cement Hydration via Voxel Swin Transformer AU - Gao, Haozhong AU - Zhang, Liangliang AU - Han, Yamin AU - Han, Ruiqi AU - Wang, Lin AU - Yang, Bo T2 - Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26 PB - International Joint Conferences on Artificial Intelligence Organization PY - 2026 SP - 1106 EP - 1114 DO - 10.24963/ijcai.2026/124 UR - https://doi.org/10.24963/ijcai.2026/124 ER -Copied
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