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• W3134640950 abstract "Recent deep networks have achieved good performance on a variety of 3d points classification tasks. However, these models often face challenges in wild tasks.There are considerable differences between the labeled training/source data collected by one Lidar and unseen test/target data collected by a different Lidar. Unsupervised domain adaptation (UDA) seeks to overcome such a problem without target domain labels.Instead of aligning features between source data and target data,we propose a method that use a Generative adversarial network to generate synthetic data from the source domain so that the output is close to the target domain.Experiments show that our approach performs better than other state-of-the-art UDA methods in three popular 3D object/scene datasets (i.e., ModelNet, ShapeNet and ScanNet) for cross-domain 3D objects classification." @default.
• W3134640950 created "2021-03-15" @default.
• W3134640950 creator A5055944363 @default.
• W3134640950 creator A5073241561 @default.
• W3134640950 date "2021-02-15" @default.
• W3134640950 modified "2023-09-27" @default.
• W3134640950 title "Generation for Adaption: A GAN-Based Approach for 3D Domain Adaption with Point Cloud Data." @default.
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• W3134640950 hasPublicationYear "2021" @default.
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