[1] [1] Yang J L, Li H D, Campbell D, et al. Go-ICP: a globally optimal solution to 3D ICP point-set registration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(11): 2241-2254.

[2] [2] Lu J, Wang W, Shao H X, et al. Point cloud registration algorithm fusing of super4PCS and ICP based on the key points[C]//2019 Chinese Control Conference (CCC). Guangzhou: IEEE, 2019: 4439-4444.

[3] [3] Qi C R, Su H, Mo K, et al. PointNet: deep learning on point Sets for 3D classification and segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 652-660.

[4] [4] Qi C R, Yi L, Su H, et al. PointNet++: deep hierarchical feature learning on point sets in a metric space[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach: Curran Associates Inc., 2017: 5105-5114.

[5] [5] Qin Z, Yu H, Wang C J, et al. Geometric transformer for fast and robust point cloud registration[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans: CVF, 2022: 11143-11152.

[6] [6] Thomas H, Qi C R, Deschaud J E, et al. KPConv: flexible and deformable convolution for point clouds[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. Seoul: CVF, 2019: 6411-6420.

[7] [7] Lin T Y, Dollr P, Girshick R, et al. Feature pyramid networks for object detection[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu: IEEE, 2017: 936-944.

[8] [8] Ren K Y, Yan T, Xu Z X, et al. Image attention transformer network for indoor 3D object detection[J]. Science China Technological Sciences, 2024, 67(7): 2176-2190.

[9] [9] Wei M Q, Wei Z Y, Zhou H R, et al. AGConv: adaptive graph convolution on 3D point clouds[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(8): 9374-9392.

[10] [10] Wang C, Zhong C. Adaptive feature pyramid networks for object detection[J]. IEEE Access, 2021, 9: 107024-107032.

[11] [11] Wu W X, Qi Z A, Fuxin L. PointConv: deep convolutional networks on 3D point clouds[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: CVF, 2019: 9621-9630.

[14] [14] Choy C, Park J, Koltun V. Fully convolutional geometric features[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. Seoul: CVF, 2019: 8958-8966.

[15] [15] Bai X Y, Luo Z X, Zhou L, et al. D3feat: joint learning of dense detection and description of 3D local features[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: CVF, 2020: 6359-6367.

[16] [16] Ao S, Hu Q Y, Yang B, et al. SpinNet: learning a general surface descriptor for 3D point cloud registration[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville: CVF, 2021: 11753-11762.

[17] [17] Huang S Y, Gojcic Z, Usvyatsov M, et al. PREDATOR: registration of 3D point clouds with low overlap[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville: CVF, 2021: 4267-4276.