Toward High-Fidelity 3D Point-Cloud Learning for Brain Folding Morphology Prediction Using Trans-Unet
This paper proposes Trans-Unet, a framework that transforms 3D point-cloud data into a 2D grid domain and integrates CNNs with self-attention mechanisms to accurately predict brain folding patterns with high fidelity and accuracy.
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[Submitted on 23 Jul 2026]
Title:Toward High-Fidelity 3D Point-Cloud Learning for Brain Folding Morphology Prediction Using Trans-Unet
View a PDF of the paper titled Toward High-Fidelity 3D Point-Cloud Learning for Brain Folding Morphology Prediction Using Trans-Unet, by Geran Zhao and 6 other authors
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Abstract:Learning high-fidelity point-cloud features in the 3D space poses significant challenges, including permutation invariance, lack of local context, difficulty in fine-grained surface reconstruction, and high computational cost. In this article, we propose Trans-Unet, a novel framework that addresses these issues by first tansforming 3D point-cloud data into a 2D grid domain and then employing a U-shaped hybrid model that integrates Convolutional Neural Networks, and self-attention mechanisms. The proposed Trans-Unet effectively learns and reconstructs precise features from high-resolution 3D point-cloud data (with 40,401 points in surface and 2,382 points in fiber) derived from a predefined finite element brain patch growth model, enabling accurate prediction of brain folding patterns. By combining multiple techniques, Trans-Unet leverages the complementary strengths: the 3D-to-2D transformation preserves fine-grained structural information while significantly reducing computational cost and the curse of dimensionality; convolutional blocks capture hierarchical, low-level local representations; and the self-attention mechanism models global, high-level semantics and long-range dependencies. The dataset consists of 3D point-clouds containing both brain surface patches and fiber information generated by a large-scale finite element model. Trans-Unet is applied to predict brain surface folding from the initial state (state 0 or states 0-2) to the final state (state 3). Experimental results demonstrate that Trans-Unet achieves high-resolution predictions of brain patch growth, surpassing existing methods in both fidelity and accuracy.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV); Methodology (stat.ME); Machine Learning (stat.ML)
Cite as: arXiv:2607.21840 [cs.CV]
(or arXiv:2607.21840v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.21840
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Geran Zhao [view email] [v1] Thu, 23 Jul 2026 22:08:11 UTC (2,460 KB)
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