As the basic task of point cloud analysis, classification is fundamental but always challenging. To address some unsolved problems of existing methods, we propose a network that captures geometric features of point clouds for better representations. To achieve this, on the one hand, we enrich the geometric information of points in low-level 3D space explicitly. On the other hand, we apply CNN-based structures in high-level feature spaces to learn local geometric context implicitly. Specifically, we leverage an idea of error-correcting feedback structure to capture the local features of point clouds comprehensively. Furthermore, an attention module based on channel affinity assists the feature map to avoid possible redundancy by emphasizing its distinct channels. The performance on both synthetic and real-world point clouds datasets demonstrate the superiority and applicability of our network. Comparing with other state-of-the-art methods, our approach balances accuracy and efficiency.

翻译：作为点云分析的基本任务,分类是根本性的,但总是具有挑战性。为了解决现有方法中一些尚未解决的问题,我们建议建立一个网络,收集点云的几何特征,以更好地表达这些特征。为了做到这一点,我们一方面明确丰富低层三维空间点的几何信息。另一方面,我们在高层特征空间应用有线电视新闻网结构,以隐含地了解当地几何背景。具体地说,我们利用错误校正反馈结构的理念来全面捕捉点云的本地特征。此外,一个基于通道亲近性的注意模块通过强调其不同渠道,帮助地貌地图避免可能的冗余。合成和现实世界点云数据集的性能显示了我们的网络的优越性和适用性。与其他最先进的方法相比,我们的方法平衡了准确性和效率。