부산대학교 도서관

Point clouds have been widely applied to represent 3D data, with a variety of applications such as autonomous driving, augmented reality, and robotics. Since collecting a large amount of labeled 3D point cloud data for training deep learning models might not always be applicable, we propose the novel learning strategy of 3D-SelfCutMix, which advances mixed-sample data augmentation techniques while exploiting the spatial and semantic consistencies between point cloud data. Depending on the availability of label supervision, the proposed network can be realized in either self-supervised or fully-supervised manners, while both versions are shown to benefit downstream tasks. In our experiments, we consider a variety of tasks including classification and part-segmentation tasks, which sufficiently support the use of the proposed method for 3D point cloud analysis.