A learnable self-supervised task for unsupervised domain adaptation on point cloud classification and segmentation

Shaolei LIU , Xiaoyuan LUO , Kexue FU , Manning WANG , Zhijian SONG

Front. Comput. Sci. ›› 2023, Vol. 17 ›› Issue (6) : 176708

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Front. Comput. Sci. ›› 2023, Vol. 17 ›› Issue (6) : 176708 DOI: 10.1007/s11704-022-2435-4
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A learnable self-supervised task for unsupervised domain adaptation on point cloud classification and segmentation

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Shaolei LIU, Xiaoyuan LUO, Kexue FU, Manning WANG, Zhijian SONG. A learnable self-supervised task for unsupervised domain adaptation on point cloud classification and segmentation. Front. Comput. Sci., 2023, 17(6): 176708 DOI:10.1007/s11704-022-2435-4

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Landrieu L, Simonovsky M. Large-scale point cloud semantic segmentation with superpoint graphs. In: Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2018, 4558–4567

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Alliegro A, Boscaini D, Tommasi T. Joint supervised and self-supervised learning for 3D real world challenges. In: Proceedings of the 25th International Conference on Pattern Recognition. 2021, 6718–6725

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Achituve I, Maron H, Chechik G. Self-supervised learning for domain adaptation on point clouds. In: Proceedings of the IEEE Winter Conference on Applications of Computer Vision. 2021, 123–133

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Qin C, You H, Wang L, Kuo C C J, Fu Y. PointDAN: a multi-scale 3D domain adaption network for point cloud representation. In: Proceedings of the 33rd International Conference on Neural Information Processing Systems. 2019, 646

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Zou L, Tang H, Chen K, Jia K. Geometry-aware self-training for unsupervised domain adaptation on object point clouds. In: Proceedings of 2021 IEEE/CVF International Conference on Computer Vision. 2021, 6383–6392

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Sauder J, Sievers B. Self-supervised deep learning on point clouds by reconstructing space. In: Proceedings of the 33rd International Conference on Neural Information Processing Systems. 2019, 1161

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