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SAP-Net: A Simple and Robust 3D Point Cloud Registration Network Based on Local Shape Features
Article

Multiple Cylinder Extraction from Organized Point Clouds

1
Department of Electrical and Computer Engineering, Faculty of Science and Engineering, Laval University, Quebec, QC G1V0A6, Canada
2
Computer Vision and Systems Laboratory (CVSL), Laval University, Quebec, QC G1V0A6, Canada
3
Robotics Laboratory, Department of Mechanical Engineering, Faculty of Science and Engineering, Laval University, Quebec, QC G1V0A6, Canada
*
Author to whom correspondence should be addressed.
Academic Editor: Stefano Berretti
Sensors 2021, 21(22), 7630; https://doi.org/10.3390/s21227630
Received: 11 October 2021 / Revised: 7 November 2021 / Accepted: 11 November 2021 / Published: 17 November 2021
(This article belongs to the Special Issue Sensing and Processing for 3D Computer Vision)
Most man-made objects are composed of a few basic geometric primitives (GPs) such as spheres, cylinders, planes, ellipsoids, or cones. Thus, the object recognition problem can be considered as one of geometric primitives extraction. Among the different geometric primitives, cylinders are the most frequently used GPs in real-world scenes. Therefore, cylinder detection and extraction are of great importance in 3D computer vision. Despite the rapid progress of cylinder detection algorithms, there are still two open problems in this area. First, a robust strategy is needed for the initial sample selection component of the cylinder extraction module. Second, detecting multiple cylinders simultaneously has not yet been investigated in depth. In this paper, a robust solution is provided to address these problems. The proposed solution is divided into three sub-modules. The first sub-module is a fast and accurate normal vector estimation algorithm from raw depth images. With the estimation method, a closed-form solution is provided for computing the normal vector at each point. The second sub-module benefits from the maximally stable extremal regions (MSER) feature detector to simultaneously detect cylinders present in the scene. Finally, the detected cylinders are extracted using the proposed cylinder extraction algorithm. Quantitative and qualitative results show that the proposed algorithm outperforms the baseline algorithms in each of the following areas: normal estimation, cylinder detection, and cylinder extraction. View Full-Text
Keywords: organized point clouds; depth map; surface normal estimation; cylinder detection; cylinder extraction organized point clouds; depth map; surface normal estimation; cylinder detection; cylinder extraction
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MDPI and ACS Style

Moradi, S.; Laurendeau, D.; Gosselin, C. Multiple Cylinder Extraction from Organized Point Clouds. Sensors 2021, 21, 7630. https://doi.org/10.3390/s21227630

AMA Style

Moradi S, Laurendeau D, Gosselin C. Multiple Cylinder Extraction from Organized Point Clouds. Sensors. 2021; 21(22):7630. https://doi.org/10.3390/s21227630

Chicago/Turabian Style

Moradi, Saed, Denis Laurendeau, and Clement Gosselin. 2021. "Multiple Cylinder Extraction from Organized Point Clouds" Sensors 21, no. 22: 7630. https://doi.org/10.3390/s21227630

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