论文标题
PU-MFA：通过多尺度的点云向上采样特征
PU-MFA : Point Cloud Up-sampling via Multi-scale Features Attention
论文作者
论文摘要
最近，随着3D扫描仪技术的开发，使用点云的研究一直在增加。根据这一趋势，对高质量点云的需求正在增加，但是获得高质量点云的高成本仍然存在问题。因此，随着深度学习的最新发展，Point Cloud Up采样研究使用深度学习来从低质量点云中产生高质量的点云，这是吸引大量关注的领域之一。本文提出了一种新的点云上采样方法，称为Point Cloud通过多尺度提出的功能（PU-MFA）。受到以前的研究的启发，这些研究使用多尺度特征或注意力机制报告了良好的性能，PU-MFA通过U-NET结构融合了两者。此外，PU-MFA适应地使用多尺度功能来有效地完善全局功能。通过使用PU-GAN数据集（这是合成点云数据集）和Kitti数据集（是实扫描的点云数据集），将PU-MFA的性能与其他最先进的方法进行了比较。在各种实验结果中，与其他最先进的方法相比，PU-MFA在定量和定性评估方面表现出了卓越的性能，证明了所提出的方法的有效性。还对PU-MFA的注意力图进行了可视化，以显示多尺度特征的效果。
Recently, research using point clouds has been increasing with the development of 3D scanner technology. According to this trend, the demand for high-quality point clouds is increasing, but there is still a problem with the high cost of obtaining high-quality point clouds. Therefore, with the recent remarkable development of deep learning, point cloud up-sampling research, which uses deep learning to generate high-quality point clouds from low-quality point clouds, is one of the fields attracting considerable attention. This paper proposes a new point cloud up-sampling method called Point cloud Up-sampling via Multi-scale Features Attention (PU-MFA). Inspired by previous studies that reported good performance using the multi-scale features or attention mechanisms, PU-MFA merges the two through a U-Net structure. In addition, PU-MFA adaptively uses multi-scale features to refine the global features effectively. The performance of PU-MFA was compared with other state-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 the real-scanned point cloud dataset. In various experimental results, PU-MFA showed superior performance in quantitative and qualitative evaluation compared to other state-of-the-art methods, proving the effectiveness of the proposed method. The attention map of PU-MFA was also visualized to show the effect of multi-scale features.
