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Recently, research using point clouds has been increasing with thedevelopment of 3D scanner technology. According to this trend, the demand forhigh-quality point clouds is increasing, but there is still a problem with thehigh cost of obtaining high-quality point clouds. Therefore, with the recentremarkable development of deep learning, point cloud up-sampling research,which uses deep learning to generate high-quality point clouds from low-qualitypoint clouds, is one of the fields attracting considerable attention. Thispaper proposes a new point cloud up-sampling method called Point cloudUp-sampling via Multi-scale Features Attention (PU-MFA). Inspired by previousstudies that reported good performance using the multi-scale features orattention mechanisms, PU-MFA merges the two through a U-Net structure. Inaddition, PU-MFA adaptively uses multi-scale features to refine the globalfeatures effectively. The performance of PU-MFA was compared with otherstate-of-the-art methods through various experiments using the PU-GAN dataset,which is a synthetic point cloud dataset, and the KITTI dataset, which is thereal-scanned point cloud dataset. In various experimental results, PU-MFAshowed superior performance in quantitative and qualitative evaluation comparedto other state-of-the-art methods, proving the effectiveness of the proposedmethod. The attention map of PU-MFA was also visualized to show the effect ofmulti-scale features.

PU-MFA : Point Cloud Up-sampling via Multi-scale Features Attention