利用人类大脑识别和处理复杂数据的非凡能力对研究人员来说是一个重大挑战,特别是在点云分类领域-一种旨在复制大脑神经结构以进行空间识别的技术。最初的3D点云数据通常会受到噪音的影响,稀疏,和混乱,使准确分类成为一项艰巨的任务,特别是在提取本地信息特征时。因此,在这项研究中,我们提出了一种新颖的基于注意力的端到端点云下采样分类方法,被称为Pointas,这是一种实验性算法,旨在适应各种下游任务。PointAS由两个主要模块组成:自适应采样模块和注意模块。具体来说,注意模块将全局特征与输入点云数据聚合,而自适应模块提取局部特征。在点云分类任务中,我们的方法在很大程度上超过了现有的向下抽样方法,允许更精确地提取边缘数据点,以准确地捕获整体轮廓特征。在各种抽样比率下,PointAS的分类精度始终超过80%,即使在超高采样率下,也具有75.37%的显著精度。此外,我们的方法在实验中表现出鲁棒性,在不同的噪声干扰下,分类精度保持在72.50%或更高。定性和定量实验都肯定了我们的方法在抽样分类任务中的有效性,为研究人员提供了一种更准确的方法来识别和分类神经元,突触,和其他结构,从而促进对神经系统的更深入的了解。
Harnessing the remarkable ability of the human brain to recognize and process complex data is a significant challenge for researchers, particularly in the domain of point cloud classification-a technology that aims to replicate the neural structure of the brain for spatial recognition. The initial 3D point cloud data often suffers from noise, sparsity, and disorder, making accurate classification a formidable task, especially when extracting local information features. Therefore, in this study, we propose a novel attention-based end-to-end point cloud downsampling classification method, termed as PointAS, which is an experimental algorithm designed to be adaptable to various downstream tasks. PointAS consists of two primary modules: the adaptive sampling module and the attention module. Specifically, the attention module aggregates global features with the input point cloud data, while the adaptive module extracts local features. In the point cloud classification task, our method surpasses existing downsampling methods by a significant margin, allowing for more precise extraction of edge data points to capture overall contour features accurately. The classification accuracy of PointAS consistently exceeds 80% across various sampling ratios, with a remarkable accuracy of 75.37% even at ultra-high sampling ratios. Moreover, our method exhibits robustness in experiments, maintaining classification accuracies of 72.50% or higher under different noise disturbances. Both qualitative and quantitative experiments affirm the efficacy of our approach in the sampling classification task, providing researchers with a more accurate method to identify and classify neurons, synapses, and other structures, thereby promoting a deeper understanding of the nervous system.