Next Article in Journal
A Gamification-Based Approach on Indoor Wayfinding Research
Previous Article in Journal
Spatial Exploration of Economic Data—Insight into Attitudes of Students towards Interdisciplinary Knowledge
Open AccessArticle

Sharp Feature Detection as a Useful Tool in Smart Manufacturing

Faculty of Mechanical Engineering, Brno University of Technology, 616 69 Brno, Czech Republic
Faculty of Business and Economics, Mendel University in Brno, 613 00 Brno, Czech Republic
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(7), 422;
Received: 12 May 2020 / Revised: 19 June 2020 / Accepted: 27 June 2020 / Published: 30 June 2020
(This article belongs to the Special Issue Advanced Research Based on Multi-Dimensional Point Cloud Analysis)
Industry 4.0 comprises a wide spectrum of developmental processes within the management of manufacturing and chain production. Presently, there is a huge effort to automate manufacturing and have automatic control of the production. This intention leads to the increased need for high-quality methods for digitization and object reconstruction, especially in the area of reverse engineering. Commonly used scanning software based on well-known algorithms can correctly process smooth objects. Nevertheless, they are usually not applicable for complex-shaped models with sharp features. The number of the points on the edges is extremely limited due to the principle of laser scanning and sometimes also low scanning resolution. Therefore, a correct edge reconstruction problem occurs. The same problem appears in many other laser scanning applications, i.e., in the representation of the buildings from airborne laser scans for 3D city models. We focus on a method for preservation and reconstruction of sharp features. We provide a detailed description of all three key steps: point cloud segmentation, edge detection, and correct B-spline edge representation. The feature detection algorithm is based on the conventional region-growing method and we derive the optimal input value of curvature threshold using logarithmic least square regression. Subsequent edge representation stands on the iterative algorithm of B-spline approximation where we compute the weighted asymmetric error using the golden ratio. The series of examples indicates that our method gives better or comparable results to other methods. View Full-Text
Keywords: edge detection; point cloud; reverse engineering; spline; industry 4.0; 3D scanning; 3D model edge detection; point cloud; reverse engineering; spline; industry 4.0; 3D scanning; 3D model
Show Figures

Graphical abstract

MDPI and ACS Style

Prochazkova, J.; Procházka, D.; Landa, J. Sharp Feature Detection as a Useful Tool in Smart Manufacturing. ISPRS Int. J. Geo-Inf. 2020, 9, 422.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop