[1] Geiger A, Lenz P, Urtasun R. Are we ready for autonomous driving? The KITTI vision benchmark suite[C], 3354-3361(2012).

[2] Wu C Y, Johnson J, Malik J et al. Multiview compressive coding for 3D reconstruction[C], 9065-9075(2023).

[3] Jin R J, Wang K, Liu M H et al. DETR with improved denoising training for multi-scale oriented object detection in optical remote sensing images (invited)[J]. Laser & Optoelectronics Progress, 61, 0211023(2024).

[4] Zhang X Z, Shen T, Xu D. Remote-sensing image object detection based on improved YOLOv8 algorithm[J]. Laser & Optoelectronics Progress, 61, 1028001(2024).

[5] Li H Y, Sima C H, Dai J F et al. Delving into the devils of bird’s-eye-view perception: a review, evaluation and recipe[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46, 2151-2170(2024).

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

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

[8] Hu J, An Y P, Xu W C et al. 3D object detection based on deep semantics and position information fusion of laser point cloud[J]. Chinese Journal of Lasers, 50, 1010003(2023).

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

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

[11] Qi C R, Litany O, He K M et al. Deep Hough voting for 3D object detection in point clouds[C], 9276-9285(2019).

[12] 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).

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

[14] 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).

[15] He C H, Zeng H, Huang J Q et al. Structure aware single-stage 3D object detection from point cloud[C], 11870-11879(2020).

[16] Chen Y L, Liu S, Shen X Y et al. Fast point R-CNN[C], 9774-9783(2019).

[17] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C], 7132-7141(2018).

[18] Woo S, Park J, Lee J Y et al. CBAM: convolutional block attention module[M]. Computer vision-ECCV 2018, 11211, 3-19(2018).

[19] Shenga H L, Cai S J, Liu Y et al. Improving 3D object detection with channel-wise transformer[C], 2723-2732(2021).

[20] Gu F W, Lu J, Cai C T et al. EANTrack: an efficient attention network for visual tracking[J]. IEEE Transactions on Automation Science and Engineering, 21, 5911-5928(2024).

[21] Gu F W, Lu J, Cai C T. RPformer: a robust parallel transformer for visual tracking in complex scenes[J]. IEEE Transactions on Instrumentation and Measurement, 71, 5011214(2022).

[22] Vaswani A, Shazeer N, Parmar N et al. Attention is all you need[EB/OL]. https://arxiv.org/abs/1706.03762

[23] Geiger A, Lenz P, Stiller C et al. Vision meets robotics: the KITTI dataset[J]. The International Journal of Robotics Research, 32, 1231-1237(2013).

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

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

[26] Shi S S, Wang Z, Shi J P et al. From points to parts: 3D object detection from point cloud with part-aware and part-aggregation network[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43, 2647-2664(2021).