%0 Journal Article %A SUN Liujie %A ZHAO Jin %A WANG Wenju %A ZHANG Yusen %T Multi-Scale Transformer Lidar Point Cloud 3D Object Detection %D 2022 %R 10.3778/j.issn.1002-8331.2109-0489 %J Computer Engineering and Applications %P 136-146 %V 58 %N 8 %X Point cloud 3D object detection has low detection accuracy for small objects such as pedestrians and bicycles, which is easy to miss detection and false detection. A 3D object detection method MSPT-RCNN(multi-scale point transformer-RCNN) based on multi-scale point cloud transformer is proposed to improve the detection accuracy of point cloud 3D objects. The method consists of two stages, the first stage(RPN) and the second stage(RCNN). In RPN stage, point cloud features are extracted through multi-scale transformer network, which includes multi-scale neighborhood embedding module and jump connection offset attention module to obtain multi-scale neighborhood geometric information and different levels of global semantic information, and generate high-quality initial 3D bounding box. In the RCNN stage, the multi-scale neighborhood geometric information of point cloud in the bounding box is introduced to optimize the position, size, orientation and confidence of the bounding box. The experimental results show that this method(MSPT-RCNN) has high detection accuracy, especially for distant and small objects. MSPT-RCNN can effectively improve the accuracy of 3D object detection by effectively learning the multi-scale geometric information in point cloud data and extracting different levels of effective semantic information. %U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2109-0489