Reconsider the Template Mesh in Deep Learning-based Mesh Reconstruction
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912385496776704 |
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| author | Zhang, Fengting Liang, Boxu Liu, Qinghao Liu, Min Chen, Xiang Wang, Yaonan |
| author_facet | Zhang, Fengting Liang, Boxu Liu, Qinghao Liu, Min Chen, Xiang Wang, Yaonan |
| contents | Mesh reconstruction is a cornerstone process across various applications, including in-silico trials, digital twins, surgical planning, and navigation. Recent advancements in deep learning have notably enhanced mesh reconstruction speeds. Yet, traditional methods predominantly rely on deforming a standardised template mesh for individual subjects, which overlooks the unique anatomical variations between them, and may compromise the fidelity of the reconstructions. In this paper, we propose an adaptive-template-based mesh reconstruction network (ATMRN), which generates adaptive templates from the given images for the subsequent deformation, moving beyond the constraints of a singular, fixed template. Our approach, validated on cortical magnetic resonance (MR) images from the OASIS dataset, sets a new benchmark in voxel-to-cortex mesh reconstruction, achieving an average symmetric surface distance of 0.267mm across four cortical structures. Our proposed method is generic and can be easily transferred to other image modalities and anatomical structures. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_15285 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reconsider the Template Mesh in Deep Learning-based Mesh Reconstruction Zhang, Fengting Liang, Boxu Liu, Qinghao Liu, Min Chen, Xiang Wang, Yaonan Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Mesh reconstruction is a cornerstone process across various applications, including in-silico trials, digital twins, surgical planning, and navigation. Recent advancements in deep learning have notably enhanced mesh reconstruction speeds. Yet, traditional methods predominantly rely on deforming a standardised template mesh for individual subjects, which overlooks the unique anatomical variations between them, and may compromise the fidelity of the reconstructions. In this paper, we propose an adaptive-template-based mesh reconstruction network (ATMRN), which generates adaptive templates from the given images for the subsequent deformation, moving beyond the constraints of a singular, fixed template. Our approach, validated on cortical magnetic resonance (MR) images from the OASIS dataset, sets a new benchmark in voxel-to-cortex mesh reconstruction, achieving an average symmetric surface distance of 0.267mm across four cortical structures. Our proposed method is generic and can be easily transferred to other image modalities and anatomical structures. |
| title | Reconsider the Template Mesh in Deep Learning-based Mesh Reconstruction |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.15285 |