3D Large-Scale Point Cloud Semantic Segmentation Using Optimal Feature Description Vector Network: OFDV-Net

Abstract

Efficient semantic segmentation of large-scale 3D point clouds is a fundamental and essential capability for real-time intelligent systems, such as autonomous driving and augmented reality. The high dimension feature vector and the complex network structure are two major constraints to utilize the large-scale point cloud. This paper proposes an optimal feature description vector network (OFDV-Net) for 3D point cloud semantic segmentation. First, a multiscale point cloud feature extraction structure is constructed to generate an initial feature description vector (IFDV). Then, IFDV is selected by a feature selection unit to obtain the optimal feature description vector (OFDV). The OFDV encapsulates the best 3D features set of the points and can be used as the input of the deep neural network for training and testing. Finally, the OFDV-Net was applied to the standard public outdoor large-scale point cloud datasets Semantic3D and NPM3D, and the overall segmentation accuracy of 88.3% and 87.7% were obtained, respectively; moreover, the OFDV-Net requires less training time, which indicates that the algorithm can obtain high-precision semantic segmentation results on an outdoor large-scale point cloud while reducing model training time.

Keywords

• Optimal feature description vector
• point cloud
• feature selection
• semantic segmentation
• deep learning