Abstract: For the problem of object detection from 3D point clouds, a high-precision and real-time single-stage deep neural network is proposed, which includes new solutions in three aspects: network feature extraction, loss function design and data augmentation. Firstly, the point clouds are directly voxelized to build a bird's eye view (BEV). In the step of feature extraction, the residual structure is used to extract high-level semantic features, and the multi-level features are combined to output dense feature map. While regressing the bounding boxes of objects from the BEV, the quadratic offset is considered in the loss function to achieve the convergence with higher precision. In training process, data augmentation is adopted by mixing 3D point clouds from different frames to improve the generalization of the network. The experimental results based on the KITTI BEV object detection dataset show that the proposed network only using the position information of the lidar point cloud, is not only better than the state-of-the-art BEV object detection network in performance, but also outperforms the methods that fuse images and point clouds. And the speed of the entire network reaches 20 frame/s, which meets the real-time requirement.