Error-Bounded Generative Models for 3D Scan Completion

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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.