PointAS: an attention based sampling neural network for visual perception

IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Frontiers in Computational Neuroscience Pub Date : 2024-05-02 DOI:10.3389/fncom.2024.1340019
Bozhi Qiu, Sheng Li, Lei Wang
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Abstract

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.
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PointAS:基于注意力的视觉感知采样神经网络
利用人脑识别和处理复杂数据的非凡能力是研究人员面临的一项重大挑战,尤其是在点云分类领域--一种旨在复制大脑神经结构进行空间识别的技术。初始的三维点云数据往往存在噪声、稀疏性和无序性,这使得准确分类成为一项艰巨的任务,尤其是在提取局部信息特征时。因此,在本研究中,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,称为 PointAS,它是一种实验性算法,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力模块。具体来说,注意力模块将全局特征与输入的点云数据聚合在一起,而自适应模块则提取局部特征。在点云分类任务中,我们的方法大大超越了现有的下采样方法,可以更精确地提取边缘数据点,从而准确捕捉整体轮廓特征。在不同的采样率下,PointAS 的分类准确率始终保持在 80% 以上,即使在超高采样率下,准确率也高达 75.37%。此外,我们的方法在实验中表现出很强的鲁棒性,在不同的噪声干扰下都能保持 72.50% 或更高的分类准确率。定性和定量实验都肯定了我们的方法在采样分类任务中的功效,为研究人员识别和分类神经元、突触和其他结构提供了更准确的方法,从而促进了对神经系统的深入了解。
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来源期刊
Frontiers in Computational Neuroscience
Frontiers in Computational Neuroscience MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
5.30
自引率
3.10%
发文量
166
审稿时长
6-12 weeks
期刊介绍: Frontiers in Computational Neuroscience is a first-tier electronic journal devoted to promoting theoretical modeling of brain function and fostering interdisciplinary interactions between theoretical and experimental neuroscience. Progress in understanding the amazing capabilities of the brain is still limited, and we believe that it will only come with deep theoretical thinking and mutually stimulating cooperation between different disciplines and approaches. We therefore invite original contributions on a wide range of topics that present the fruits of such cooperation, or provide stimuli for future alliances. We aim to provide an interactive forum for cutting-edge theoretical studies of the nervous system, and for promulgating the best theoretical research to the broader neuroscience community. Models of all styles and at all levels are welcome, from biophysically motivated realistic simulations of neurons and synapses to high-level abstract models of inference and decision making. While the journal is primarily focused on theoretically based and driven research, we welcome experimental studies that validate and test theoretical conclusions. Also: comp neuro
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