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Three-dimensional (3D) object classification tasks using point clouds are widely used in 3D modeling, face recognition, and robotic missions. However, processing raw point clouds directly is problematic for a traditional convolutional network due to the irregular data format of point clouds. This paper proposes a pointwise convolution neural network (CNN) structure that can process point cloud data directly without preprocessing. First, a 2D convolutional layer is introduced to perceptdoi:10.3745/jips.02.0160 dblp:journals/jips/SongLTF21 fatcat:jeq2oyvb4rhupgcqzuntygrg4u