[1] Qi C R, Su H, Mo K, et al. Point: deep learning on point sets f 3D classification segmentation[C]Proceedings of the IEEE conference on computer vision pattern recognition(CVPR). 2017: 652660.

[2] Qi C R, Yi L, Su H, et al. Point++: deep hierarchical feature learning on point sets in a metric space[C]Advances in Neural Infmation Processing Systems (NIPS 2017). New Yk: Curran Assosciates, Inc, 2017: 51055114.

[3] Li Y, Bu R, Sun M, et al. PointCNN: convolution on Χtransfmed points[C]Conference Wkshop on Neural Infmation Processing Systems(NIPS), 2018: 820830.

[4] Thomas H, Qi C R, Deschaud JE, et al. KPConv: flexible defmable convolution f point clouds[C]Proceedings of the IEEECVF International Conference on Computer Vision(ICCV), 2019: 64116420.

[5] Zhou Y, Tuzel O. Voxel: endtoend learning f point cloud based 3D object detection[C]Proceedings of the IEEECVF Conference on Computer Vision Pattern Recognition (CVPR), 2018: 44904499.

[6] Lang A H, Va S, Caesar H, et al. Pointpillars: fast encoders f object detection from point clouds[C]Proceedings of the IEEECVF Conference on Computer Vision Pattern Recognition(CVPR), 2019: 1269712705.

[7] X Zhu, H Zhou, T Wang, et al. Cylindrical and asymmetrical 3D convolution networks for lidar segmentation.

[8] Tang H, Liu Z, Zhao S, et al. Searching efficient 3D architectures with sparse pointvoxel convolution[C]European Conference on Computer Vision(ECCV), 2020: 685702.

[9] J Xu, R Zhang, J Dou, et al. RPVNet: A deep and efficient range-point-voxel fusion network for lidar point cloud segmentation.

[10] a S, Lang A H, Helou B, et al. Pointpainting: sequential fusion f 3d object detection[C]Proceedings of the IEEE Conference on Computer Vision Pattern Recognition, 2020: 46044612.

[11] Wang C, Ma C, Zhu M, et. al. PointAugmenting: crossmodal augmentation f 3D object detection[C]Proceedings of the IEEECVF Conference on Computer Vision Pattern Recognition (CVPR), 2021: 1179411803.

[12] Liang M, Yang B, Wang S, et al. Deep continuous fusion f multisens 3D object detection[C]European Conference on Computer Vision(ECCV), 2018: 663678.

[13] F Duerr, H Weigel, J Beyerer. Decoupled iterative deep sensor fusion for 3D semantic segmentation. International Journal of Semantic Computing, 15, 293-312(2021).

[14] Cheng R, Razani R, Taghavi E, et al. (AF)2S3: attentive feature fusion with adaptive feature ion f sparse semantic segmentation wk[C]Proceedings of the IEEE Conference on Computer Vision Pattern Recognition, 2021: 1254712556.

[15] He K, Zhang X. Ren S, et al. Deep residual learning f image recognition[C]Proceedings of the IEEE Conference on Computer Vision Pattern Recognition, 2016: 770778.

[16] Liu Z, Lin Y T, Cao Y, et al. Swin Transfmer: Hierarchical vision transfmer using shifted windows [C]Proceedings of the IEEECVF International Conference on Computer Vision(ICCV), 2021: 999210002.

[17] Philion J, LiftFidler S. Lift, splat, shoot: encoding images from arbitrary camera rigs by implicitly unprojecting to 3D [C]European Conference on Computer Vision(ECCV), 2020: 194210.

[18] Hu J, Shen L, Sun G. Squeezeexcitation wks[C]Proceedings of the IEEE Conference on Computer Vision Pattern Recognition, 2018: 71327141.

[19] X Qin, Z Zhang, C Huang, et al. U2-Net: going deeper with nested U-structure for salient object detection. Pattern Recognition, 106, 107404(2020).

[20] Zhang H Y, Cisse M, Dauphin Y N, et al. Mixup: beyond empirical risk minimization [C]Proceedings of the International Conference on Learning Representations(ICLR), 2018: 113.

[21] Hong M, Choi J, Kim G. StyleMix: separating content style f enhanced data augmentation[C]Proceedings of the IEEECVF Conference on Computer Vision Pattern Recognition (CVPR), 2021: 1486214870.

[22] Kim J H, Choo W, Ssong H. Puzzle mix: exploiting saliency local statistics f optimal mixup[C]International Conference on Machine Learning(ICML), 2020: 52755285.

[23] Yun S, Han D, Chun S, et al. CutMix: regularization strategy to train strong classifiers with localizable features[C]2019 IEEECVF International Conference on Computer Vision (ICCV), 2019: 60226031.

[24] Xu Chenfeng, Wu Bichen, Wang Zining, et. al. Squeezesegv3: spatiallyadaptive convolution f efficient pointcloud segmentation [C]European Conference on Computer Vision(ECCV), 2020: 119.