Abstract
Point cloud classification plays a significant role in Light Detection and Ranging (LiDAR) applications. However, most available multi-scale feature learning networks for large-scale 3D LiDAR point cloud classification tasks are time-consuming. In this paper, an efficient deep neural architecture denoted as Point Expanded Multi-scale Convolutional Network (PEMCNet) is developed to accurately classify the 3D LiDAR point cloud. Different from traditional networks for point cloud processing, PEMCNet includes successive Point Expanded Grouping (PEG) units and Absolute and Relative Spatial Embedding (ARSE) units for representative point feature learning. The PEG unit enables us to progressively increase the receptive field for each observed point and aggregate the feature of a point cloud at different scales but without increasing computation. The ARSE unit following the PEG unit furthermore realizes representative encoding of points relationship, which effectively preserves the geometric details between points. We evaluate our method on both public datasets (the Urban Semantic 3D (US3D) dataset and Semantic3D benchmark dataset) and our new collected Unmanned Aerial Vehicle (UAV) based LiDAR point cloud data of the campus of Guangdong University of Technology. In comparison with four available state-of-the-art methods, our methods ranked first place regarding both efficiency and accuracy. It was observed on the public datasets that with a 2% increase in classification accuracy, over 26% improvement of efficiency was achieved at the same time compared to the second efficient method. Its potential value is also tested on the newly collected point cloud data with over 91% of classification accuracy and 154 ms of processing time.
Highlights
The advent of Light Detection and Ranging (LiDAR) technology provides an effective way to acquire 3D spatial data in the form of point clouds
Concerning classification accuracy, the two accuracy metrics of overall accuracy (OA) and mIoU were both observed with the highest values among all the results
The mIoU of our method increased by 2% and OA increased by 1% upon that achieved by the method ranked in second place
Summary
The advent of Light Detection and Ranging (LiDAR) technology provides an effective way to acquire 3D spatial data in the form of point clouds. Voxelization converts the point cloud data of a discrete structure into the form of a continuous domain so that the voxelized point clouds can be directly processed by 3D convolution operations and the deep learning methods can be applied to point cloud classification through indirect means. This approach has shown good performance, it suffers from high memory consumption due to the sparsity of the voxels. As the point cloud classification technique becoming increasingly sophisticated, deep neural networks that can be directly implemented to process raw point clouds has received increased attention
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