[1] Zhao L, Hu J, Liu H et al. Deep learning was based on semantic segmentation for three-dimensional object detection from point clouds[J]. Chinese Journal of Lasers, 48, 1710004(2021).

[2] Wang W, Xu Y, Ren Y C et al. Parsing of urban facades from 3D point clouds based on a novel multi-view domain[J]. Photogrammetric Engineering & Remote Sensing, 87, 283-293(2021).

[3] Kundu A, Yin X Q, Fathi A et al. Virtual multi-view fusion for 3D semantic segmentation[M]. Vedaldi A, Bischof H, Brox T, et al. Computer vision-ECCV 2020. Lecture notes in computer science, 12369, 518-535(2020).

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

[6] Xu Y F, Fan T Q, Xu M Y et al. Spidercnn: deep learning on point sets with parameterized convolutional filters[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11212, 87-102(2018).

[7] Zhang Z Y, Hua B S, Chen W et al. Global context-aware convolutions for 3D point cloud understanding[C], 210-219(2020).

[8] Maturana D, Scherer S. VoxNet: a 3D Convolutional Neural Network for real-time object recognition[C], 922-928(2015).

[9] Kumar K S C, Al-Stouhi S. Multi-scale voxel class-balanced ASPP for LIDAR pointcloud semantic segmentation[C], 117-124(2021).

[10] Park J, Kim C, Kim S et al. PCSCNet: Fast 3D semantic segmentation of LiDAR point cloud for autonomous car using point convolution and sparse convolution network[J]. Expert Systems With Applications, 212, 118815(2023).

[11] Graham B. Spatially-sparse convolutional neural networks[EB/OL]. https://arxiv.org/abs/1409.6070

[12] Riegler G, Ulusoy A O, Geiger A. OctNet: learning deep 3D representations at high resolutions[C], 6620-6629(2017).

[13] Engelcke M, Rao D, Wang D Z et al. Vote3Deep: Fast object detection in 3D point clouds using efficient convolutional neural networks[C], 1355-1361(2017).

[14] Graham B, Engelcke M, Maaten L V D. 3D semantic segmentation with submanifolds sparse convolutional networks[C], 9224-9232(2018).

[15] Wang X L, Girshick R, Gupta A et al. Non-local neural networks[C], 7794-7803(2018).

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

[17] Woo S, Park J, Lee J Y et al. CBAM: convolutional block attention module[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11211, 3-19(2018).

[18] Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation[M]. Navab N, Hornegger J, Wells W M, et al. Medical image computing and computer-assisted intervention-MICCAI 2015. Lecture notes in computer science, 9351, 234-241(2015).

[19] He T, Shen C H, van den Hengel A. DyCo3D: robust instance segmentation of 3D point clouds through dynamic convolution[C], 354-363(2021).

[20] Zhu Z, Xu M D, Bai S et al. Asymmetric non-local neural networks for semantic segmentation[C], 593-602(2019).

[21] Dai A, Chang A X, Savva M et al. ScanNet: richly-annotated 3D reconstructions of indoor scenes[C], 2432-2443(2017).

[22] Armeni I, Sax S, Zamir A R et al. Joint 2D-3D-semantic data for indoor scene understanding[EB/OL]. https://arxiv.org/abs/1702.01105

[23] Xu M T, Ding R Y, Zhao H S et al. PAConv: position adaptive convolution with dynamic kernel assembling on point clouds[C], 3172-3181(2018).

[24] Choy C, Gwak J, Savarese S. 4D spatio-temporal ConvNets: minkowski convolutional neural networks[C], 3070-3079(2019).

[25] Thomas H, Qi C R, Deschaud J E et al. KPConv: flexible and deformable convolution for point clouds[C], 6410-6419(2019).