Deep multi-scale learning on point sets for 3D object recognition

Abstract

In recent years, point cloud data based 3D deep learning has become a popular method for three-dimensional object recognition. In this work, we introduce a multi-scale convolution neural network which takes point cloud as input for 3D object recognition. Our network structure consists of two parts which are the feature extraction structure and the feature processing part. Experiments are conducted on the ModelNet40 dataset with several state-of-the-art methods. The proposed method achieves a higher accuracy on 3D object recognition with 87.1%. Experimental results have demonstrated the superior performance of the proposed multi-scale feature learning network.