Deep learning network for point cloud classification based on k-dimensional tree neighbor query

Abstract

PointNet++ is one of the effective deep learning methods to deal with the point cloud classification. However, there are some problems in this method including low precision, being time-consuming and sensitive to the noise of input points. In order to deal with these problems, we propose an efficient neighbor query method in which a k-dimensional tree (kd tree) structure is constructed to find the neighbor points within a specified radius around the query points, and the local features are extracted at the grouping layer of PointNet++. Aiming at the overfitting problem existing in the original network training process, we introduce the dropout regularization and thus reduce the training time of network convergence. The experiment environment is TensorFlow framework under Ubuntu14.04 system. The training and testing experiments are conducted based on ModelNet40 dataset. The classification accuracy of this method reaches 91.1%, 92.1% and 94.3% when the query radius is 0.1, 0.2 and 0.3, which is higher than PointNet++, and the kd tree method takes less time than the original method. At the same time, the improved structure has better semantic segmentation performance on the Stanford 3D semantic analysis dataset (mean intersection over union reaches 57.2%), which demonstrates the proposed method being more robust to occlusion objects.