[1] R Q CHARLES, S HAO, K C MO et al. PointNet: deep learning on point sets for 3D classification and segmentation, 77-85(2017).
[3] Y WANG, Y B SUN, Z W LIU et al. Dynamic graph CNN for learning on point clouds. ACM Transactions on Graphics, 38, 1-12(2019).
[4] Y LI, R BU, M SUN et al. PointCNN: Convolution on Χ-transformed points, 31(2018).
[5] Y R SHEN, C FENG, Y Q YANG et al. Mining point cloud local structures by kernel correlation and graph pooling, 4548-4557(2018).
[6] W X WU, Z A QI, F X LI. PointConv: deep convolutional networks on 3D point clouds, 9613-9622(2019).
[7] H THOMAS, C R QI, J E DESCHAUD et al. KPConv: flexible and deformable convolution for point clouds, 6410-6419(2019).
[8] M T XU, R Y DING, H S ZHAO et al. PAConv: position adaptive convolution with dynamic kernel assembling on point clouds, 3172-3181(2021).
[9] A HALEVY, P NORVIG, F PEREIRA. The unreasonable effectiveness of data. IEEE Intelligent Systems, 24, 8-12(2009).
[10] C SUN, A SHRIVASTAVA, S SINGH et al. Revisiting unreasonable effectiveness of data in deep learning era, 843-852(2017).
[11] Y Chen, V T Hu, E Gavves et al. Pointmixup: augmentation for point clouds, 330-345(28).
[12] A KRIZHEVSKY, I SUTSKEVER, G E HINTON. ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60, 84-90(2017).
[13] Y LECUN, L BOTTOU, Y BENGIO et al. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86, 2278-2324(1998).
[14] F J MORENO-BAREA, F STRAZZERA, J M JEREZ et al. Forward noise adjustment scheme for data augmentation, 728-734(2018).
[16] H INOUE. Data augmentation by pairing samples for images classification. arXiv preprint(2018).
[17] Z ZHONG, L ZHENG, G L KANG et al. Random erasing data augmentation. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 13001-13008(2020).
[21] S YUN, D HAN, S CHUN et al. CutMix: Regularization strategy to train strong classifiers with localizable features, 6022-6031(2019).
[22] A CRESWELL, T WHITE, V DUMOULIN et al. Generative adversarial networks: an overview. IEEE Signal Processing Magazine, 35, 53-65(2018).
[24] S V SHESHAPPANAVAR, V V SINGH, C KAMBHAMETTU. PatchAugment: local neighborhood augmentation in point cloud classification, 2118-2127(2021).
[25] S KIM, S LEE, D HWANG et al. Point cloud augmentation with weighted local transformations, 528-537(2021).
[26] R H LI, X Z LI, P A HENG et al. PointAugment: an auto-augmentation framework for point cloud classification, 6377-6386(2020).
[27] J ZHANG, L CHEN, B OUYANG et al. Pointcutmix: Regularization strategy for point cloud classification. Neurocomputing, 505, 58-67(2022).
[28] D LEE, J LEE, J LEE et al. Regularization strategy for point cloud via rigidly mixed sample, 15895-15904(2021).
[30] J H KIM, W CHOO, H O SONG. Puzzle Mix: exploiting saliency and local statistics for optimal mixup, 5275-5285(2020).
[31] S L HUANG, X C WANG, D C TAO. SnapMix: semantically proportional mixing for augmenting fine-grained data, 35, 1628-1636(2021).
[32] S LEE, M JEON, I KIM et al. Sagemix: saliency-guided mixup for point clouds, 35, 23580-23592(2022).
[33] C H LEE, A VARSHNEY, D W JACOBS. Mesh saliency, 659-666(2005).
[34] AX CHANG, T FUNKHOUSER, L GUIBAS et al. ShapeNet: an information-rich 3d model repository. arXiv preprint(2015).