Abstract Now 3D point cloud classificaiton is widely applied in many domains, including robot operation, automous driving and virtual reality. Extracting rich features with high discrimination is the key to 3D point cloud classification. Therefore, an algorithm of 3D point cloud classification based on local-nonlocal interactive convolution is designed to improve the feature extraction of point cloud. Firstly, a local-nonlocal interactive convolution module is constructed. After obtaining local and nonlocal similar features, interactive enhancement is employed to alleviate the redundancy problem caused by a single neighborhood representing a closed region. Consequently, the hierarchy and stability of the network are enhanced and the degradation problem of the network is alleviated. Then, the convolution neural network is constructed with the module as the basic unit. Finally, adaptive feature fusion is adopted to make full use of different levels of features to realize 3D point cloud classification. Experimental results on two benchmark datasets, ModelNet40 and ScanObjectNN, show that the proposed method generates better performance.
Fund:Supported by National Natural Science Foundation of China(No.62032022,62006215)
Corresponding Authors:
CAO Feilong, Ph.D., professor. His research interests include deep learning and image processing.
About author:: LU Xinyu, master student. His research interests include deep learning and point cloud analysis.YANG Bing, Ph.D., lecturer. His research interests include deep learning and image processing.YE Hailiang, Ph.D., lecturer. His research interests include deep learning and image processing.
LU Xinyu,YANG Bing,YE Hailiang等. 3D Point Cloud Classification Based on Local-Nonlocal Interactive Convolution[J]. Pattern Recognition and Artificial Intelligence, 2022, 35(2): 141-149.
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