点云
计算机科学
杠杆(统计)
冗余(工程)
人工智能
特征(语言学)
点(几何)
特征向量
模式识别(心理学)
数据挖掘
数学
几何学
语言学
操作系统
哲学
作者
Shi Qiu,Saeed Anwar,Nick Barnes
标识
DOI:10.1109/tmm.2021.3074240
摘要
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.
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