Implementation of robust algorithms and softgauges for dense point clouds partitioning: application to traceable 3D scanners H/F

Job description

Topic description

Analysis of scanning systems needs

3D laser scanning systems are vital for the digital transformation of geometric measurements.

They are widely used in economically significant fields as diverse as mechanical engineering, construction, or cargo logistics, for example.

Data acquisition can be well integrated and fully automated into industrial workflows.

They are often the surface measurement tool of choice to create digital representations of manufactured parts or structures of interest – a technique that the Directorate “Prosperity” of the European Commission’s Directorate-General for Research and Innovation identified as one of six enabling technologies supporting industry 5.0 [1].

But the fundamental output of scanning systems is, compared to classical dimensional measurements, not more than large amounts of 3D coordinate data points whose individual measurement uncertainty is, in general, to be considered worse than those of more classical approaches.

A sophisticated large data volume post-process incorporating data fusion, partitioning, analysis and interpretation is then needed to extract the application specific product.

A sound quantitative assessment of the uncertainty of the measurement product based on a reasonable time and analysis effort is considered the holy grail, enabling an even broader use also in more critical application fields.

Fundamental advance in fields such as mechanical engineering, construction, or cargo logistics only be ensured, if generalisable, holistic and thus practical solutions for the metrological characterisation of the 3D scanning measurement can be achieved.

Point clouds are the intermediate product between the physical measurement and the feature measurement.

In 3D scanning, routine scans can comprise millions of data points.

Fusion or registration are further processing steps on the primary data.

They are often captured from multiple scans made from different locations to unspecified surface locations.

This introduces novel sources of uncertainty while potentially reducing the impact of the individual coordinate uncertainty on the final measurement result.

Application of classical approaches to uncertainty modelling of these datasets lead to variance-covariance matrices with excessive dimensions of millions by millions and are impractical to handle.

Less specialised users desperately need a more practical approach, possibly focussing on the metadata relevant for the final measurement uncertainty (e.g. the uncertainty of individual point of point clouds).

The property of interest is in most cases is not the point cloud itself, but derived characteristics such as inclination, volume, or manufacturing defects in in-line inspection, using either geometric or radiometric features.

The amount of data generated is often challenging and requires dedicated computing architecture as well as sophisticated data-analysis approaches, often using proprietary software tools.

Data analysis complexity, as well as limited interoperability of different laser scanning platforms are considered major challenges to a broader application, e.g., in architecture, engineering, and construction [2].

In their draft strategic research agenda, the EMN Advanced Manufacturing calls for further reference datasets for software validation in manufacturing quality control and sees the need for data driven software validation against complex geometrical surfaces and components [3].

Since a good number of available computational software give differing results, reference data for validation are urgently needed, not only for industrial applications, but also at National Metrology Institutes (NMIs) and Designed Institutes.

Context and state of the art

Due to the availability of several commercial and open-source software solutions, automated 3D reconstruction methods are becoming popular.

Nonetheless, the metrological and reliability aspects of the resulting 3D scanning and modelling should not be ignored, particularly when the adoption of such solutions are not only dedicated to rapid 3D modelling and visualisation, but also to accurate scanning purposes related to tangible industrial applications.

For many software solutions, performance in terms of computational costs is the prime target followed closely by visual impression.

Stability and robustness to 3D scanning errors are of lesser concern.

Stability means that the underlying numerical operations are numerically stable, while robustness refers to the software ability to handle extreme cases, for example systematic bias due to laser reflections when scanning cylindrical holes.

Established software tests in coordinate metrology are currently based on small datasets with simple geometric features and limited number of data points.

These tests do not sufficiently cover the challenges of a laser scanner generated 3D point cloud evaluation, for example where surfaces being captured are not Lambertian in their reflectance.

Within the selected European project 24DIT04-ScanClouDT, two approaches will be pursued to develop reference datasets for the metrological verification of point cloud processing software packages.

Procedures and good practice will be developed to generate empirical reference datasets from experimental data generated employing dedicated artefacts and high-quality scanning systems.

The focus of the project with respect to this objective will be on the development of a numerical generator of validation reference data for huge 3D datasets.

Independent knowledge of the ground truth, for example from tactile probing systems, allows the evaluation of completeness and accuracy of respective software analyses.

At least three verification datasets for the three different use cases (i.e., aerospace industry, logistics, and geodesy) will be developed.

While the first experimental approach enables users to generate verification data close to the application by themselves according to established rules, the second approach can pave the way to a respective verification service provided by European NMIs.

Reference data generation and robust point cloud partitioning algorithms

Recent advances in 3D laser scanning systems allow complex objects and structures to be surveyed and reconstructed using a large set of scanned cloud points (millions of points).

This thesis aims to investigate and make available robust reference methods for the whole trustworthy processing of dense scanned cloud points.

The addressed and implemented reference processing methods could be validated on numerous reference datasets to ensure fusion, 3D reconstruction, partitioning, classification, evaluation, etc., with uncertainties well below the achieved scanning uncertainty.

Reference data for validating reference, commercial and open-source software for dense cloud points processing defined by sets of data points for which the exact value of deviation is known with an associated uncertainty.

The method for reference data generation enables the control of several parameters in the generated datasets including but not limited to, the density and distribution of the points, the value of the error/deviation, the number of datasets, the initial poses, etc.

The aim of flawless reference data use is to make sure that the software returns correct values with an uncertainty below the nanometre level.

Even if the establishment of a good reference dataset generator with an accuracy which is two to three times better than the expected software results is not trivial, it remains an essential and unavoidable task for the validation of software.

Partitioning is a fundamental operation defined in ISO GPS standard which aims at decomposing a part into independent features or surface portions for further processing and analysis.

Default partitioning process is used to decompose the surface into independent features from invariance classes while non-default partitioning is used to create compound features [4].

Attributes of the point cloud such as curvature, normal, slippage, etc.

are calculated in order to provide criteria for different methods [5].

Classical methods for default partitioning include edge detection, region growing, clustering, spectral analysis, shape fitting and statistical evaluation [6].

Then, non-default partitioning can be solved by invariance subgroup reasoning [7].

However, user interaction is often needed to improve the robustness and avoid over- or under-segmentations.

With the rapid development of Artificial Neural Networks related techniques in computer vision, data-driven methods are developing increasingly for automated and efficient point cloud processing without user interaction [7] [8].

Specific objectives of the thesis

Reference

[1] M.

Breque et al., ‘Industry 5.0 – Towards a sustainable, human-centric and resilient European industry’, Publications Office of the European Union,

[2] A.

Waqar et al., ‘Complexities for adopting 3D laser scanners in the AEC industry: Structural equation modelling’, Applications in Engineering Science 16,

[3] ‘EMN for Advanced Manufacturing Key Industry Sector Topics: Draft Strategic Research Agenda December ’,

[4] ISO -3 Geometrical product specifications (GPS) — Partitioning — Part 3: Methods used for Specification and Verification.

International Organization for Standardization, Geneva

[5] A.

Shamir, ‘A survey on mesh segmentation techniques’.

Comput.

Graph.

Forum 27, -

[6] Anwer N., Scott P.J., Srinivasan, ‘Toward a Classification of Partitioning Operations for Standardization of Geometrical Product Specifications and Verification’, Procedia CIRP, 75, -

[7] Y.

Qie, N.

Anwer, ‘Toward non-default partitioning for compound feature identification in engineering design’, Procedia CIRP, , –

[8] Y.

Qie, N.

Anwer, ‘Invariance Class based Surface Reconstruction using CNN Transfer Learning’, Computer-Aided Design & Applications, 20, -

[9] Y.

Qie, L.

Qiao, N.

Anwer, ‘A Framework for Curvature-Based CAD Mesh Partitioning’, Lecture Notes in Mechanical Engineering, -

The proposed thesis is part of EURAMET via the EMP 24DIT04-ScanClouDT project.

Start date : 01/09/

Duration : 36 mois

Location: The thesis will take place primarily at the LNE in Paris and LURPA at ENS Paris-Saclay.

The framework of the European project will allow for scientific exchanges with the project consortium as well as travel within Europe during the thesis.

Funding category

Funding further details

Required Skill Profession

Computer Occupations