An Effective Encoding Method Based on Local Information for 3D Point Cloud Classification

Abstract

Point cloud is a collection of many unordered points. Deep learning network encounters difficulties in utilizing the local information of point cloud because of its irregular format. This is not conducive to the network to identify the details of the object. Some strategies that design new network structures are used to capture the local information, but they make the networks become complicated. This paper proposes an effective method by encoding points into feature vectors. The local information is represented using neighborhood points that are searched by the k-nearest neighbor method. The neighborhood points are converted into feature elements based on the distance between these points and the encoded point. The feature vector of each point consists of its coordinates and the corresponding feature elements. A simple deep learning network is used to process these feature vectors. The proposed method is applied to ModelNet40 shape classification benchmark. The experimental results show that the classification accuracy of the simple deep learning network is improved by the proposed method.