DFAN: Dual-Branch Feature Alignment Network for Domain Adaptation on Point Clouds

Unsupervised domain adaptation (UDA) significantly reduces the gap between the source domain and the target domain in machine learning and computer vision tasks. Most UDA approaches are applied to images and videos, and only a few methods implement domain adaptation on 3-D computer vision problems....

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Veröffentlicht in:	IEEE transactions on geoscience and remote sensing 2022, Vol.60, p.1-12
Hauptverfasser:	Shi, Liangwei, Yuan, Zhimin, Cheng, Ming, Chen, Yiping, Wang, Cheng
Format:	Artikel
Sprache:	eng
Schlagworte:	3-D computer vision Ablation Adaptation Adaptation models Alignment Cloud computing Commonality Computer vision Deep learning domain adaptation Domains Feature extraction Image segmentation Machine learning point cloud classification Point cloud compression point cloud segmentation Task analysis Three dimensional models Three-dimensional displays Visual tasks
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