3D-DaVa: Enhancing 3D Point Cloud Data Reliability for Industrial Applications

The escalating incorporation of three-dimensional (3D) point cloud data across industrial applications highlights the necessity of assuring its reliability. The error-prone process of object digitization, the large data volumes, and equipment inaccuracies can lead to the degradation of 3D point cloud data quality by introducing varying degrees of noise, outliers, and missing values. Therefore, there is a pressing need for a generally applicable, comprehensive, and robust solution to effectively validate the integrity of point cloud data and assess its quality. Such a solution would empower professionals to rely on this data for critical decision-making, covering applications within many domains, such as manufacturing, automotive, and robotics. In this article, we propose, apply, and assess the 3D Point Cloud Data Validation (3D-DaVa) pipeline, an automated 3D data validation system incorporating statistical and machine learning techniques. The pipeline takes a point cloud and its reference as input and outputs accuracy, validity, and completeness scores. We demonstrated the efficacy of 3D-DaVa through a rigorous evaluation using both real-world manufacturing data and openly available data, where we deliberately introduced distortions covering five distortion levels by simulating common inaccuracies. The data quality assessment results obtained by varying distortion levels reveal a decreasing trend with a distortion increase, thus underscoring the 3D-DaVa’s capability to quantify such deviations accurately.