Error-Bounded Generative Models for 3D Scan Completion

现代机器学习数据分析保证了快速、灵活的数据处理，降低了成本，降低了许多3D数据采集任务(如激光扫描)的进入门槛。当尝试将数据预处理步骤合并到诸如创建数字双胞胎之类的下游任务中时，经常面临的一个问题是测量系统生成的几何图形的精度和可靠性。对于生成/处理的3D结构精度的精度具有可靠的误差界是这些技术在诸如能源和建筑等市场中适应的关键使能/障碍。数据社区的标准实践是在单独的验证数据集上测量性能。这为ML模型的性能提供了一些迹象，然而，围绕几何模型可靠性的总体问题仍然存在。Visio ImPulse(https://www.visioimpulse.com)将与国家物理实验室，NPL密切合作，探索用于测量ML生成的3D结构的准确性和可靠性的各种技术，目的是为几何敏感过程提供确定性。NPL提供的专业知识和仪器将用于确定测量几何误差的正确方法和围绕3D结构建模技术的确定性范围，对模型中各种元素的依赖性和敏感性以及对数据采集方法的敏感性。这将允许开发3D数据采集和后处理的优化(混合)策略，并将进行测试以显示一致的性能，从而提高采用这些技术的障碍。

Modern Machine Learning data analysis promise fast and flexible data processing, allowing reduced cost and lower barrier to entry for many 3D data acquisition tasks (e.g. laser scans). One problem regularly faced when trying to incorporate data pre-processing steps into downstream tasks such as creation of Digital Twins is to measure the accuracy and reliability of the system generated geometry.Having solid error bounds for the accuracy of the generated/processed 3D structure accuracy is the key enabler/barrier to adaptation of these techniques in markets such as Energy and Construction.The standard practice in data community is to measure performance on a separate validation dataset. This gives some indication into the performance of the ML models however, the overall question around the reliability of the geometric model remains. In addition, the best practice for selecting an unbiased and representative validation set is currently not well understood.Visio Impulse (https://www.visioimpulse.com) will work closely with the National Physical Laboratory, NPL, to explore various techniques for measuring accuracy and reliability of the ML generated 3D structure with the aim of providing certainty for geometry sensitive processes.The expertise and instrumentation available at NPL will be used to determine the correct approach to measuring geometric errors and the certainty bounds around the 3D structure modelling techniques, the dependencies and sensitivity to various elements within the model as well as the sensitivity to data acquisition approaches.This will allow the development of optimised (hybrid) strategy for 3D data acquisition and post processing and will be tested to show consistent performance, thus elevating the barriers to adoption of these techniques.