[1] WU Z, SONG S, KHOSLA A, et al. 3D shapenets: a deep representation for volumetric shapes[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 7-12, 2015, Boston, MA, USA. New York: IEEE, 2015: 1912-1920.
[2] MATURANA D, SCHERER S. Voxnet: a 3D convolutional neural network for real-time object recognition[C]//2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), September 28-October 2, 2015, Hamburg, Germany. New York: IEEE, 2015: 922-928.
[3] QI C R, SU H, NIE?NER M, et al. Volumetric and multi-view CNNs for object classification on 3D data[C]//Proceedings of the IEEE Conference on ComputerVision and Pattern Recognition, June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 2016: 5648-5656.
[4] SU H, MAJI S, KALOGERAKIS E, et al. Multi-view convolutional neural networks for 3D shape recognition[C]//Proceedings of the IEEE International Conference on Computer Vision, December 7-13, 2015, Santiago, Chile. New York: IEEE, 2015: 945-953.
[5] BOULCH A, LE SAUX B, AUDEBERT N. Unstructured point cloud semantic labeling using deep segmentation networks[J]. 3DOR, 2017, 3: 17-24.
[6] LAWIN F J, DANELLJAN M, TOSTEBERG P, et al. Deep projective 3D semantic segmentation[C]//Computer Analysis of Images and Patterns: 17th International Conference, August 22-24, 2017, Ystad, Sweden. Berlin, Heidelberg: Springer, 2017: 95-107.
[7] 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, July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 2017: 652-660.
[8] QI C R, YI L, SU H, et al. PointNet++: deep hierarchicalfeature learning on point sets in a metric space[J]. Advances in neural information processing systems, 2017, 30: 5100-5110.
[9] LI Y, BU R, SUN M, et al. PointCNN: convolution on X-transformed points[J]. Advances in neural information processing systems, 2018, 31: 820-830.
[10] 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, October 27-November 2, 2019, Seoul, Korea (South). New York: IEEE, 2019: 6411-6420.
[11] XU M, DING R, ZHAO H, et al. PAConv: position adaptive convolution with dynamic kernel assembling on point clouds[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition,June 20-25, 2021, Nashville, TN, USA. New York: IEEE, 2021: 3173-3182.
[12] XU Y, FAN T, XU M, et al. SpiderCNN: deep learning on point sets with parameterized convolutional filters[C]//Proceedings of the European Conference on Computer Vision, September 8-14, 2018, Munich, Germany. Berlin, Heidelberg: Springer, 2018: 87-102.
[13] LI K, ZHANG Z, ZHONG C, et al. Robust structured declarative classifiers for 3D point clouds: defending adversarial attacks with implicit gradients[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-24, 2022, New Orleans, LA, USA. New York: IEEE, 2022: 15294-15304.
[14] CHEN J, YANG M, VELIPASALAR S. ViewNet: a novel projection-based backbone with view pooling for few-shot point cloud classification[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-22, 2023, Vancouver, BC, Canada. New York: IEEE, 2023: 17652-17660.
[15] ZHANG J, JIA J, LIU H, et al. PointCert: point cloud classification with deterministic certified robustness guarantees[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-22, 2023, Vancouver, BC, Canada. New York: IEEE, 2023: 9496-9505.
[16] WANG X, GIRSHICK R, GUPTA A, et al. Non-local neural networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE, 2018: 7794-7803.
[17] LIU Z, LIN Y, CAO Y, et al. Swin transformer: hierarchical vision transformer using shifted windows[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, October 10-17, 2021, Montreal, QC, Canada. New York: IEEE, 2021: 10012-10022.
[18] ZHENG S, LU J, ZHAO H, et al. Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition,June 20-25, 2021, Nashville, TN, USA. New York: IEEE, 2021: 6881-6890.
[19] WANG Y, SUN Y, LIU Z, et al. Dynamic graph CNN for learning on point clouds[J]. ACM transactions on graphics (tog), 2019, 38(5): 1-12.
[20] KLOKOV R, LEMPITSKY V. Escape from cells: deep KD-networks for the recognition of 3D point cloud models[C]//Proceedings of the IEEE International Conference on Computer Vision, October 22-19, 2017, Venice, Italy. New York: IEEE, 2017: 863-872.
[21] WU W, QI Z, FUXIN L. Pointconv: deep convolutional networks on 3D point clouds[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 15-20, 2019, Long Beach, CA, USA. New York: IEEE, 2019: 9621-9630.
[22] JIANG L, ZHAO H, LIU S, et al. Hierarchical point-edge interaction network for point cloud semantic segmentation[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, October 27-November 2, 2019, Seoul, Korea (South). New York: IEEE, 2019: 10433-10441.
[23] ATZMON M, MARON H, LIPMAN Y. Point convolutionalneural networks by extension operators[EB/OL]. (2028-03-27) [2023-06-24]. https://arxiv.org/abs/1803. 10091v1.
[24] ENGLE N, BELAAIANNIS V, DIETMAYER K. Point transformer[J]. IEEE access, 2021, 9: 134826-134840.
[25] GIANCOLA S, ZARZAR J, GHANEM B. Leveraging shape completion for 3D siamese tracking[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 15-20, 2019, Long Beach, CA, USA. New York: IEEE, 2019: 1359-1368.
[26] QI H, FENG C, CAO Z, et al. P2B: point-to-box networkfor 3D object tracking in point clouds[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 13-19, 2020, Seattle, WA, USA. New York: IEEE, 2020: 6329-6338.
[27] ZHENG C, YAN X, GAO J, et al. Box-aware feature enhancement for single object tracking on point clouds[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, October 10-17, 2021, Montreal, QC, Canada. New York: IEEE, 2021: 13199-13208.
[28] CUI Y, FANG Z, SHAN J, et al. 3D object tracking with transformer[EB/OL]. (2021-10-28) [2023-06-24]. https://arxiv.org/abs/2110.14921.
[29] HUI L, WANG L, CHENG M, et al. 3D Siamese voxel-to-BEV tracker for sparse point clouds[J]. Advances in neural information processing systems, 2021,34: 28714-28727.
[30] ZHOU C, LUO Z, LUO Y, et al. PTTR: relational 3D point cloud object tracking with transformer[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-24, 2022, New Orleans, LA, USA. New York: IEEE, 2022: 8531-8540.
[31] MENZE M, GEIGER A. Object scene flow for autonomous vehicles[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,June 7-12, 2015, Boston, MA, USA. New York: IEEE, 2015: 3061-3070.
[32] YANG Z, SUN Y, LIU S, et al. 3DSSD: point-based 3D single stage object detector[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 13-19, 2020, Seattle, WA, USA. New York: IEEE, 2020: 11040-11048.
[33] ZHANG Y, HU Q, XU G, et al. Not all points are equal: learning highly efficient point-based detectors for 3D lidar point clouds[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition,June 18-24, 2022, New Orleans, LA, USA. New York: IEEE, 2022: 18953-18962.
