• français
    • English
    français
  • Login
Help
View Item 
  •   Home
  • Laboratoire d’Ingénierie des Systèmes Physiques Et Numériques (LISPEN)
  • View Item
  • Home
  • Laboratoire d’Ingénierie des Systèmes Physiques Et Numériques (LISPEN)
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Machine learning-based 3D scan coverage prediction for smart-control applications

Article dans une revue avec comité de lecture
Author
LI, Tingcheng
543315 Laboratoire d’Ingénierie des Systèmes Physiques et Numériques [LISPEN]
ccPOLETTE, Arnaud
543315 Laboratoire d’Ingénierie des Systèmes Physiques et Numériques [LISPEN]
LOU, Ruding
543315 Laboratoire d’Ingénierie des Systèmes Physiques et Numériques [LISPEN]
JUBERT, Manon
543315 Laboratoire d’Ingénierie des Systèmes Physiques et Numériques [LISPEN]
NOZAIS, Dominique
ccPERNOT, Jean-Philippe
543315 Laboratoire d’Ingénierie des Systèmes Physiques et Numériques [LISPEN]
1003434 Arts et Métiers Sciences et Technologies

URI
http://hdl.handle.net/10985/26194
DOI
10.1016/j.cad.2024.103775
Date
2024-11
Journal
Computer-Aided Design

Abstract

Automatic control of a workpiece being manufactured is a requirement to ensure in-line correction and thus move towards a more intelligent manufacturing system. There is therefore a need to develop control strategies which are capable of taking precise account of real working conditions and enabling first-time-right control. As part of such a smart-control strategy, this paper introduces a machine learning-based approach capable of accurately predicting a priori the 3D coverage of a part according to a scan configuration given as input, i.e. predicting before scanning it which areas of the part will be acquired for real. This corresponds to a paradigm shift, where coverage estimation no longer relies on theoretical visibility criteria, but on rules learned from a large amount of data acquired in real-life conditions. The proposed 3D Scan Coverage Prediction Network (3DSCP-Net) is based on a 3D feature encoding and decoding module, which is capable of taking into account the specifics of the scan configuration whose impact on the 3D coverage is to be predicted. To take account of real working conditions, features are extracted at various levels, including geometric ones, but also features characterising the way structured-light projection behaves. The method is thus able to incorporate inter-reflection and overexposure issues into the prediction process. The database used for the training was built using an ad-hoc platform specially designed to enable the automatic acquisition and labelling of numerous point clouds from a wide variety of scan configurations. Experiments on several parts show that the method can efficiently predict the scan coverage, and that it outperforms conventional approaches based on purely theoretical visibility criteria.

Files in this item

Name:
LISPEN_CAD_2024_PERNOT_1.pdf
Size:
4.002Mb
Format:
PDF
View/Open

Collections

  • Laboratoire d’Ingénierie des Systèmes Physiques Et Numériques (LISPEN)

Related items

Showing items related by title, author, creator and subject.

  • Machine learning-based 3D scan coverage prediction for smart-control applications 
    Article dans une revue avec comité de lecture
    ccLI, Tingcheng; ccPOLETTE, Arnaud; ccLOU, Ruding; JUBERT, Manon; NOZAIS, Dominique; ccPERNOT, Jean-Philippe (Elsevier BV, 2024-07-20)
    Automatic control of a workpiece being manufactured is a requirement to ensure in-line correction and thus move towards a more intelligent manufacturing system. There is therefore a need to develop control strategies which ...
  • On the Use of Quality Metrics to Characterize Structured Light-based Point Cloud Acquisitions 
    Communication avec acte
    ccLI, Tingcheng; ccLOU, Ruding; ccPOLETTE, Arnaud; SHAO, Zilong; NOZAIS, Dominique; ccPERNOT, Jean-Philippe (2022)
    Even if 3D acquisition systems are nowadays more and more efficient, the resulting point clouds nevertheless contain quality defects that must be taken into account beforehand, in order to better anticipate and control ...
  • On the Use of Quality Metrics to Characterize Structured Light-based Point Cloud Acquisitions 
    Article dans une revue avec comité de lecture
    LI, Tingcheng; RUDING, Lou; DOMINIQUE, NOZAIS; ZILONG, SHAO; ccPERNOT, Jean-Philippe; ccPOLETTE, Arnaud (Computer-Aided Design & Applications, 2023-01-01)
    Even if 3D acquisition systems are nowadays more and more e cient, the resulting point clouds nevertheless contain quality defects that must be taken into account beforehand, in order to better anticipate and control ...
  • Survey on the View Planning Problem for Reverse Engineering and Automated Control Applications 
    Article dans une revue avec comité de lecture
    PEUZIN-JUBERT, Manon; NOZAIS, Dominique; MARI, Jean-Luc; ccPERNOT, Jean-Philippe; ccPOLETTE, Arnaud (Elsevier BV, 2021-12)
    At present, optical sensors are being widely used to realize high quality control or reverse engineering of products, systems, buildings, environments or human bodies. Although the intrinsic characteristics of such ...
  • On the use of quality metrics to characterize structured light-based point cloud acquisitions 
    Communication avec acte
    ccLOU, Ruding; ccPOLETTE, Arnaud; ZILONG, SHAO; DOMINIQUE, NOZAIS; ccPERNOT, Jean-Philippe (2022-07-11)
    Accurately transferring the real world to the virtual one through reverse engineering is of utmost importance in Industry 4.0 applications. Indeed, acquiring good quality 3D representations of existing physical objects ...

Browse

All SAMCommunities & CollectionsAuthorsIssue DateCenter / InstitutionThis CollectionAuthorsIssue DateCenter / Institution

Newsletter

Latest newsletterPrevious newsletters

Statistics

Most Popular ItemsStatistics by CountryMost Popular Authors

ÉCOLE NATIONALE SUPERIEURE D'ARTS ET METIERS

  • Contact
  • Mentions légales

ÉCOLE NATIONALE SUPERIEURE D'ARTS ET METIERS

  • Contact
  • Mentions légales