Lightweight deep neural network for point cloud classification

For point cloud classification, deep learning based methods use operations like voxelization to generate regular 3D grids or render the 3D mesh into a collection of images from multiple angles. However, the conversion will introduce additional computing and storage consumption. Some methods directly consume the raw point cloud. But their network scale and computational complexity make it difficult for them to deploy in embedded environments. On the basis of intensive studies of these algorithms, a novel lightweight dual path way network is proposed in this paper. Without additional conversion, our network attains a comparable performance but has 0.8 million floating parameters only. With point-wise and neighbor-wise representations, our approach incorporates global and local features of the point cloud. Experimental results on ModelNet40 and MNIST data-set demonstrate that our method achieves a good accuracy, and prove the effectiveness of our design.