首页|Point Cloud Classification Using Content-Based Transformer via Clustering in Feature Space
Point Cloud Classification Using Content-Based Transformer via Clustering in Feature Space
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国家科技期刊平台
NETL
NSTL
维普
万方数据
Recently,there have been some attempts of Trans-former in 3D point cloud classification.In order to reduce com-putations,most existing methods focus on local spatial attention,but ignore their content and fail to establish relationships between distant but relevant points.To overcome the limitation of local spatial attention,we propose a point content-based Transformer architecture,called PointConT for short.It exploits the locality of points in the feature space(content-based),which clusters the sampled points with similar features into the same class and com-putes the self-attention within each class,thus enabling an effec-tive trade-off between capturing long-range dependencies and computational complexity.We further introduce an inception fea-ture aggregator for point cloud classification,which uses parallel structures to aggregate high-frequency and low-frequency infor-mation in each branch separately.Extensive experiments show that our PointConT model achieves a remarkable performance on point cloud shape classification.Especially,our method exhibits 90.3％Top-1 accuracy on the hardest setting of ScanOb-jectNN.Source code of this paper is available at https://github.com/yahuiliu99/PointConT.
Yahui Liu、Bin Tian、Yisheng Lv、Lingxi Li、Fei-Yue Wang
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State Key Laboratory for Management and Control of Complex Systems,Institute of Automation,Chinese Academy of Sciences,Beijing 100190,and also with the School of Artificial Intelligence,University of Chinese Academy of Sciences,Beijing 100049,China
Transportation and Autonomous Systems Institute(TASI)and the Department of Electrical and Computer Engineering,Purdue School of Engineering and Technology,Indiana University-Purdue University Indianapolis(IUPUI),Indianapolis 46202 USA
国家自然科学基金国家重点研发计划Key Research and Development Program 2020 of GuangzhouKey-Area Research and Development Program of Guangdong Province
国家科技期刊平台
NSTL
维普
万方数据
