In this paper, we aim at detecting vehicles from the point clouds scanned from the urban area. Our detection method consists of a segmentation stage and a classification stage. Prior knowledge for vehicles and urban environment is utilized to help the detection process. Specifically, we incorporate curb detection and removal in the segmentation stage. Moreover, our approach is able to estimate the orientation of the candidates and use it to handle the difficult cases such as the vehicles in the parking lot. In order to distinguish the vehicles from other segments among the 3D point cloud candidates, we develop three architectures of the orthogonal-view CNN, which are based on the orthogonal view projections of the candidates. Detailed evaluations and comparisons are performed on a challenging point cloud dataset of urban area.
['Jing Huang', 'Suya You']
Vehicle detection in urban point clouds with orthogonal-view convolutional neural network