Deep Multi-level Feature Learning on Point Sets for 3D Object Recognition

Abstract

In recent years, deep learning has become an important method on point cloud for 3D object recognition. PointNet is the first neural network which could directly consume point cloud as input. However, the PointNet couldn't capture the local features. In this work, we introduce a multi-level feature extraction neural network which extracts the characteristics of the multi-level structure in PointNet. 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 89.4%. Experimental results have demonstrated the superior performance of the proposed multi-level feature learning network.