[1] Wu Y Q, Chen H X, Zhang Y. Review of 3D point cloud processing methods based on deep learning[J]. Chinese Journal of Lasers, 51, 0509001(2024).

[2] Han L, Gao Y B, Shi Z C. Three-dimensional object detection of radar point cloud based on sparse transformer[J]. Computer Engineering, 48, 104-110, 144(2022).

[3] Zhou Z G, Li Y Y, Cao J W et al. Surface target detection algorithm based on 3D lidar[J]. Laser & Optoelectronics Progress, 59, 1815006(2022).

[4] Charles R Q, Hao S, Mo K C et al. PointNet: deep learning on point sets for 3D classification and segmentation[C], 77-85(2017).

[5] Charles R Q, Yi L, Su H et al. PointNet++: deep hierarchical feature learning on point sets in a metric space[EB/OL]. https://arxiv.org/abs/1706.02413v1

[6] Charles R Q, Liu W, Wu C X et al. Frustum PointNets for 3D object detection from RGB-D data[C], 918-927(2018).

[7] Wang Z X, Jia K. Frustum ConvNet: sliding frustums to aggregate local point-wise features for amodal 3D object detection[C], 1742-1749(2019).

[8] Shi S S, Wang X G, Li H S. PointRCNN: 3D object proposal generation and detection from point cloud[C], 770-779(2019).

[9] Yang Z T, Sun Y N, Liu S et al. 3DSSD: point-based 3D single stage object detector[C], 11037-11045(2020).

[10] Zhang Y F, Hu Q Y, Xu G Q et al. Not all points are equal: learning highly efficient point-based detectors for 3D LiDAR point clouds[C], 18931-18940(2022).

[11] Zhou Y, Tuzel O. VoxelNet: end-to-end learning for point cloud based 3D object detection[C], 4490-4499(2018).

[12] Yan Y, Mao Y X, Li B. SECOND: sparsely embedded convolutional detection[J]. Sensors, 18, 3337(2018).

[13] Lang A H, Vora S, Caesar H et al. PointPillars: fast encoders for object detection from point clouds[C], 12689-12697(2019).

[14] Zheng W, Tang W L, Chen S J et al. CIA-SSD: confident IoU-aware single-stage object detector from point cloud[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 35, 3555-3562(2021).

[15] Deng J J, Shi S S, Li P W et al. Voxel R-CNN: towards high performance voxel-based 3D object detection[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 35, 1201-1209(2021).

[16] Li Z X, Li Z M, Peng D Z et al. User classification of social networks based on feature contrastive learning and graph convolution[J]. Computer Engineering, 50, 258-266(2024).

[17] Shi W J, Rajkumar R. Point-GNN: graph neural network for 3D object detection in a point cloud[C], 1708-1716(2020).

[18] Wang Y, Solomon J. Object DGCNN: 3D object detection using dynamic graphs[EB/OL]. https://arxiv.org/abs/2110.06923

[19] Wang Y, Sun Y B, Liu Z W et al. Dynamic graph CNN for learning on point clouds[J]. ACM Transactions on Graphics, 38, 146(2019).

[20] Zhao H S, Jiang L, Jia J Y et al. Point transformer[C], 16239-16248(2021).

[21] Shi S S, Jiang L, Deng J J et al. PV-RCNN++: point-voxel feature set abstraction with local vector representation for 3D object detection[J]. International Journal of Computer Vision, 131, 531-551(2023).

[22] Yang H H, Liu Z L, Wu X P et al. Graph R-CNN: towards accurate 3D object detection with semantic-decorated local graph[M]. Computer vision-ECCV 2022, 13668, 662-679(2022).

[23] Li Z C, Wang F, Wang N Y. LiDAR R-CNN: an efficient and universal 3D object detector[C], 7542-7551(2021).

[24] Mao J G, Xue Y J, Niu M Z et al. Voxel transformer for 3D object detection[C], 3144-3153(2021).

[25] Hu J, Shen L, Albanie S et al. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 2011-2023(2020).

[26] Girshick R. Fast R-CNN[C], 1440-1448(2015).

[27] Lin T Y, Goyal P, Girshick R et al. Focal loss for dense object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 318-327(2020).

[28] Xia Q M, Deng J H, Wen C L et al. Co[C]. 2023 IEEE/CVF International Conference on Computer Vision (ICCV), 6231-6240(3).

[29] Shi S S, Guo C X, Jiang L et al. PV-RCNN: point-voxel feature set abstraction for 3D object detection[C], 10526-10535(2020).

[30] Wang H Y, Shi C, Shi S S et al. DSVT: dynamic sparse voxel transformer with rotated sets[C], 13520-13529(2023).