3D MSSD: A multilayer spatial structure 3D object detection network for mobile LiDAR point clouds

Abstract

Point cloud-based object detection is vital and essential for many real-world applications, such as autonomous driving and robot vision. The PointPillars model has achieved the efficient detection of objects in front of a vehicle. However, the algorithm does not consider the spatial structures semantic information stored in the three-dimensional point cloud for a given spatial structure, thus leading to missed or false detections for objects with complex spatial structures or singular structures. We propose an approach based on PointPillars, which considers the spatial structure characteristics of 3D point clouds to enhance the detection accuracy. First, based on the specified range of the z-axis coordinates, the entire point cloud scene is divided into several layers so that the point cloud areas in the same height interval form one layer. Data from several layers are obtained. Second, the point clouds of several layers are processed with Pillar Feature Net to obtain several pseudoimages. Each pseudoimage represents the semantic information from the corresponding level of the point cloud. Third, the obtained pseudoimages from each level are merged with the pseudoimages of the entire scene to obtain a feature map with spatial structure characteristics. We apply a Region Proposal Network, and an object detection operator processes the feature map and obtains the result of object detection. Experiments show that the proposed method has a highly accurate detection effect for objects with complex spatial structures. In addition, the proposed method does not erroneously detect objects with similar semantic information after vertical dimension projection.