In minimally-invasive brain surgeries with indirect and narrow operating environments, 3D brain reconstruction is crucial. However, as requirements of accuracy for some new minimally-invasive surgeries (such as brain-computer interface surgery) are higher and higher, the outputs of conventional 3D reconstruction, such as point cloud (PC), are facing the challenges that sample points are too sparse and the precision is insufficient. On the other hand, there is a scarcity of high-density point cloud datasets, which makes it challenging to train models for direct reconstruction of high-density brain point clouds. In this work, a novel model named stereoscopic-aware graph generative adversarial network (SG-GAN) with two stages is proposed to generate fine high-density PC conditioned on a single image. The Stage-I GAN sketches the primitive shape and basic structure of the organ based on the given image, yielding Stage-I point clouds. The Stage-II GAN takes the results from Stage-I and generates high-density point clouds with detailed features. The Stage-II GAN is capable of correcting defects and restoring the detailed features of the region of interest (ROI) through the up-sampling process. Furthermore, a parameter-free-attention-based free-transforming module is developed to learn the efficient features of input, while upholding a promising performance. Comparing with the existing methods, the SG-GAN model shows superior performance in terms of visual quality, objective measurements, and performance in classification, as demonstrated by comprehensive results measured by several evaluation metrics including PC-to-PC error and Chamfer distance.

翻译：在间接且狭窄的操作环境下的微创脑外科手术中，三维脑部重建至关重要。然而，随着某些新型微创手术（如脑机接口手术）对精度的要求日益提高，传统三维重建的输出（如点云）面临采样点过于稀疏、精度不足的挑战。另一方面，高密度点云数据集的稀缺性使得直接重建高密度脑点云的模型训练困难重重。本文提出一种名为立体感知图生成对抗网络（SG-GAN）的两阶段新型模型，用于从单张图像生成精细的高密度点云。第一阶段GAN基于输入图像勾勒器官的原始形状与基本结构，生成第一阶段点云；第二阶段GAN利用第一阶段结果生成具有细节特征的高密度点云。该第二阶段GAN通过上采样过程能够修正缺陷并恢复感兴趣区域（ROI）的细节特征。此外，本文还开发了一种基于无参数注意力机制的自由变换模块，在保证优异性能的同时学习输入的有效特征。与现有方法相比，SG-GAN模型在视觉质量、客观指标测量及分类性能方面均展现出卓越表现——通过包括点对点误差和倒角距离在内的多项评估指标的全面结果验证了其优势。