Region-proposal Convolutional Network-driven Point Cloud Voxelization and Over-segmentation for 3D Object Detection

Driving environment information perception of intelligent vehicle is a major foundation for active and safe automatic driving. Perception of 3D object detection is an important basis for intelligent behavior decision-making such as obstacle recognition, collision avoidance, path planning and etc. In this paper, we present a multi-layer structured object detection framework under parallel pipeline. Our method does not rely solely on pure 3D scheme, but leverages region-proposal 2D learning classification detector and volumatic point cloud over-segmentation under 3D physical and geometric attributes for object localization. We innovative reduce the search space of 3D detection, implement multi-mode data fusion and multi-dimensional information interaction. Even scenes with different scales and different granularity knowledge can get corresponding 3D object detection and semantic information perception. Meanwhile, input with sparse data or stacked occlusion will also output certain detection feedback. Experiments show that the work of this paper can improve the accuracy and feasibility of segmentation of 3D instances in complex scenes of intelligent vehicles, and output cognitive results with location information and semantic perception information.