[1] LI Yujie, LI Xuanpeng, ZHANG Weigong. Survey on vision-based 3d object detection methods[J]. Computer Engineering and Applications, 56, 11-24(2020).

[2] MA Qinglu, BAI Feng, ZHANG Jie et al. Vehicle localization and navigation method based on lidar point cloud map[J]. Optics and Precision Engineering, 32, 2537-2549(2024).

[3] LIU Liwen, YANG Weidong, FEI Ben. GeeNet: robust and fast point cloud completion for ground elevation estimation towards autonomous vehicles[J]. Frontiers of Information Technology & Electronic Engineering, 25, 938-950(2024).

[4] ZHOU Yin, TUZEL O. Voxelnet: end-to-end learning for point cloud based 3d object detection[C], 4490-4499(2018).

[5] YAN Yan, MAO Yuxin, LI Bo. Second: sparsely embedded convolutional detection[J]. Sensors, 18, 3337(2018).

[6] GRAHAM B, ENGELCKE M, LAURENS V D M. 3D semantic segmentation with submanifold sparse convolutional networks[C], 9224-9232(2018).

[7] LANG A H, VORA S, CAESAR H et al. PointPillars: fast encoders for object detection from point clouds[C], 12697-12705(2019).

[8] JI Yimu, CHEN Zhiyu, TIAN Penghao et al. A survey of 3D target detection methods in unmanned driving[J]. Journal of Nanjing University of Posts and Telecommunications, 39, 72-79(2019).

[9] LU Mingyu, YAN Jun, GE Wancheng. Lidar point cloud based 3d object detection for self-driving: a survey[J]. Video Engineering, 46, 150-155, 173(2022).

[10] TAO Yang, ZHU Teng, ZHONG Bangqian et al. Repvits-yolox: underwater blurred and occluded target detection method[J]. Computer Engineering and Applications, 60, 200-208(2024).

[11] YU Jiaqi, YANG Honggang, WANG Yang. Point cloud vehicle target detection based on improved pointpillars[J]. Foreign Electronic Measurement Technology, 43, 69-77(2024).

[12] JI Shuyu, XIAO Shanzhu. A pillar-based lightweight 3D detector with density-enhanced pillar encoder[C], 77-81(2024).

[13] LE D T, SHI H, REZATOFIGHI H et al. Accurate and real-time 3D pedestrian detection using an efficient attentive pillar network[J]. IEEE Robotics and Automation Letters, 8, 1159-1166(2022).

[14] LIU Yudong, WANG Yongtao, WANG Siwei et al. CBNet: a novel composite backbone network architecture for object detection[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 11653-11660(2020).

[15] LIANG Tingting, CHU Xiaojie, LIU Yudong et al. CBNet: a composite backbone network architecture for object detection[J]. IEEE Transactions on Image Processing, 31, 6893-6906(2022).

[16] CHANG Liang, BAI Jie, HUANG Libo. Multi-target detection based on camera and radar feature fusion networks[J]. Journal of Beijing Institute of Technology, 42, 318-323(2022).

[17] LIN T Y, GOYAL P, GIRSHICK R et al. Focal loss for dense object detection[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2999-3007(2017).

[18] LIU Zhe, ZHAO Xin, HUANG Tengteng et al. TANet: robust 3D object detection from point clouds with triple attention[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 11677-11684(2020).

[19] HU Jie, AN Yongpeng, XU Wencai et al. 3D object detection based on deep semantics and position information fusion of laser point cloud[J]. Chinese Journal of Lasers, 50, 200-210(2023).

[20] DAI Y, GIESEKE F, OEHMCKE S et al. Attentional feature fusion[C], 3560-3569(2021).

[21] LENG Z, TAN M, LIU C et al. Polyloss: a polynomial expansion perspective of classification loss functions[J/OL]. https://arxiv.org/abs/2204.12511

[22] SHI Shaoshuai, WANG Xiaogang, LI Hongsheng. PointRCNN: 3D object proposal generation and detection from point cloud[C], 770-779(2019).