发表机构
Binghamton University（宾汉姆顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对 3D 点云学习高保真特征的挑战，提出 Trans-Unet 框架，先将 3D 点云转 2D 网格域，再用 U 形混合模型，结合多种技术优势，在预测脑折叠模式上超越现有方法，实现高分辨率预测。
AI 中文摘要
在 3D 空间中学习高保真点云特征面临诸多挑战，包括排列不变性、缺乏局部上下文、细粒度表面重建困难和高计算成本。本文提出 Trans-Unet，先将 3D 点云数据转换为 2D 网格域，再采用集成卷积神经网络和自注意力机制的 U 形混合模型。该模型能从预定义有限元脑补丁生长模型的高分辨率 3D 点云数据中有效学习和重建精确特征，准确预测脑折叠模式。通过结合多种技术，利用互补优势。数据集包含脑表面补丁和纤维信息的 3D 点云。Trans-Unet 用于预测脑表面从初始状态到最终状态的折叠。实验结果表明，它在保真度和准确性上超越现有方法，实现高分辨率预测。
英文摘要
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.
