Point Clouds Classification of Large Scenes based on Blueprint Separation Convolutional Neural Network

Abstract

In industry 4.0-related applications such as UAVs, autonomous driving, remote sensing, and navigation, environmental perception based on large-scene point clouds plays a crucial role. Accurate point clouds classification is the key and premise of environment perception. In this paper, we propose a new point clouds classification method, FeatureB2SE. First, we design a feature extraction method for point clouds by projecting features in different directions in 2D and 3D to form feature maps. Then, we present a B2SE convolution that can more adequately leverage the advantages from both blueprints separable convolution and Squeeze-and-Excitation networks. To effectively evaluate the performance of FeatureB2SE, extensive experiments have been conducted on two public datasets, GML_B and Vaihingen. The outcome demonstrates that our strategy has achieved state-of-the-art baselines. Specifically, the classification accuracy achieves 98.91% on the GML_B dataset and 85.11% on the Vaihingen dataset, respectively.