利用
计算机科学
特征(语言学)
人工智能
点云
交叉口(航空)
特征学习
模式识别(心理学)
点(几何)
代表(政治)
特征提取
数学
地理
语言学
地图学
几何学
政治
计算机安全
哲学
法学
政治学
作者
Saifullahi Aminu Bello,Cheng Wang,Naftaly Muriuki Wambugu,Jibril Muhammad Adam
标识
DOI:10.1016/j.neucom.2021.07.044
摘要
Recently, a lot of attention is given to deep learning on raw 3D point clouds. Existing approaches, however, either exploit the global shape feature without paying attention to the local features or hierarchically exploit local features with little attention to the global shape feature. In this paper, we proposed Fused Feature Point Network (FFPointNet), a deep neural network for learning on raw point clouds that exploits both local and global shape features. Specifically, we designed a novel module, ChannelNet, a simple and effective module that exploits global shape features using 1D convolutions. ChannelNet uses only 0.041 million parameters, making it easy to plug in the generic PointNet++ backbone to exploits both local and global shape structures for better contextual representation. Experiments carried out showed that by fusing PointNet++ feature with ChannelNet feature, we gained an improved classification accuracy over PointNet++ by 2.2% on the popular ModelNet40 dataset; and an improved class mean intersection over union of 1.4% on the popular ShapeNetParts dataset.
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