无需配准和重建，直接用几何特性监控点云缺陷。
Registration-Free Monitoring of Unstructured Point Cloud Data via Intrinsic Geometrical Properties
- 利用拉普拉斯与测地距离捕捉形状内在几何特征。
- 可处理数百个特征，有效识别多种缺陷类型。
- 适合复杂形状点云的实时质量监控场景。
现代传感技术可获取不同规模的无序点云数据（PCD），用于监测3D物体的几何精度，广泛应用于增材、减材及混合制造等先进制造流程。为确保分析一致性并避免误报，通常需进行配准和网格重建等预处理，但这些步骤易出错、耗时且可能引入伪影，影响监控结果。本文提出一种全新的注册自由点云监控方法，无需配准与网格重建。该方法包含两种可选的特征学习方式，均基于形状的内在几何属性，通过拉普拉斯算子和测地距离捕获特征；监控方案采用阈值筛选，从数百个特征中选出最能反映异常状态的内在特征。数值实验与案例研究验证了该方法在识别多种缺陷方面的有效性。
原文摘要 · Abstract (English)
Modern sensing technologies have enabled the collection of unstructured point cloud data (PCD) of varying sizes, which are used to monitor the geometric accuracy of 3D objects. PCD are widely applied in advanced manufacturing processes, including additive, subtractive, and hybrid manufacturing. To ensure the consistency of analysis and avoid false alarms, preprocessing steps such as registration and mesh reconstruction are commonly applied prior to monitoring. However, these steps are error-prone, time-consuming and may introduce artifacts, potentially affecting monitoring outcomes. In this paper, we present a novel registration-free approach for monitoring PCD of complex shapes, eliminating the need for both registration and mesh reconstruction. Our proposal consists of two alternative feature learning methods and a common monitoring scheme designed to handle hundreds of features. Feature learning methods leverage intrinsic geometric properties of the shape, captured via the Laplacian and geodesic distances. In the monitoring scheme, thresholding techniques are used to further select intrinsic features most indicative of potential out-of-control conditions. Numerical experiments and case studies highlight the effectiveness of the proposed approach in identifying different types of defects.
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