Point clouds are widely used to construct models of workpieces using 3D scanners, especially where high-quality robotic and automatic 3D scanning is required in industries and manufacturing. In recent years, Point Cloud Quality Assessment (PCQA) has garnered increasing attention as it provides quality scores for entire point clouds, addressing issues such as downsampling and compression distortions. However, current PCQA methods cannot provide specific and local quality scores, which are necessary to facilitate rescanning and recompletion in 3D robotic scanning. Additionally, 3D data produced by robotic view-planning algorithms are usually considered the final result, where PCQA is typically not involved. In this paper, we bridge the gap between PCQA methods and practical robotic 3D scanning. We propose a no-reference PCQA method that recognizes sparse regions during 3D scanning, providing both local and overall quality scores. Unlike traditional methods that primarily consider density as a key metric, our method assumes that an expected 3D scan will have a uniformly distributed point cloud on surfaces. We analyze the quality of points by using geometric information from surfaces fitted to these points, which are mapped to a 2D distribution based on specified distances and angles. We conducted experiments on various datasets, including both synthetic and public datasets, to evaluate the accuracy and robustness of our method. The results show that our method can represent the quality on surfaces more accurately and robustly than density calculation methods. Additionally, it outperforms most existing PCQA methods in scenarios of downsampling, which is a common challenge in high-quality 3D scanning applications. The performance of our quality enhancement experiments on practical 3D scanning, conducted towards the end of our study, demonstrates significant potential for real-world applications. The related code is released at https://github.com/leihui6/PCQA.

中文翻译：

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一种用于增强 3D 机器人扫描的稳健点云质量评估方法


点云广泛用于使用 3D 扫描仪构建工件模型，尤其是在工业和制造业需要高质量机器人和自动 3D 扫描的情况下。近年来，点云质量评估 （PCQA） 获得了越来越多的关注，因为它为整个点云提供质量分数，解决了下采样和压缩失真等问题。然而，当前的 PCQA 方法无法提供特定的局部质量分数，而这些分数对于促进 3D 机器人扫描中的重新扫描和重新完成是必要的。此外，由机器人视图规划算法生成的 3D 数据通常被视为最终结果，其中通常不涉及 PCQA。在本文中，我们弥合了 PCQA 方法和实际机器人 3D 扫描之间的差距。我们提出了一种无参考 PCQA 方法，该方法可在 3D 扫描过程中识别稀疏区域，同时提供局部和整体质量分数。与主要将密度作为关键指标的传统方法不同，我们的方法假设预期的 3D 扫描将在表面上具有均匀分布的点云。我们通过使用来自拟合到这些点的表面的几何信息来分析点的质量，这些信息根据指定的距离和角度映射到 2D 分布。我们在各种数据集上进行了实验，包括合成数据集和公共数据集，以评估我们方法的准确性和稳健性。结果表明，我们的方法可以比密度计算方法更准确、更稳健地表示表面质量。此外，在下采样场景中，它的性能优于大多数现有的 PCQA 方法，这是高质量 3D 扫描应用中的常见挑战。 我们在研究接近尾声时对实际 3D 扫描进行的质量增强实验表明，它在实际应用中具有巨大的潜力。相关代码在 https://github.com/leihui6/PCQA 发布。