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Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

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Multiple Cylinder Extraction from Organized Point Clouds.

Saed Moradi^1,2, Denis Laurendeau^1,2, Clement Gosselin³

¹Department of Electrical and Computer Engineering, Faculty of Science and Engineering, Laval University, Quebec, QC G1V0A6, Canada.

Sensors (Basel, Switzerland)

|November 27, 2021

Summary

This summary is machine-generated.

This study introduces a robust method for detecting and extracting multiple cylinders from 3D data. The new approach improves normal vector estimation and cylinder detection accuracy for 3D computer vision tasks.

Keywords:

cylinder detection cylinder extraction depth map organized point clouds surface normal estimation

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Area of Science:

Computer Vision
3D Object Recognition
Geometric Primitive Extraction

Background:

Man-made objects often consist of basic geometric primitives (GPs).
Cylinders are frequently used GPs, making their detection crucial for 3D computer vision.
Existing cylinder detection algorithms face challenges in initial sample selection and simultaneous multi-cylinder detection.

Purpose of the Study:

To develop a robust solution for cylinder detection and extraction in 3D computer vision.
To address limitations in initial sample selection for cylinder extraction.
To enable simultaneous detection of multiple cylinders within a scene.

Main Methods:

A fast and accurate normal vector estimation algorithm using raw depth images.
Utilizing Maximally Stable Extremal Regions (MSER) for simultaneous cylinder detection.
A novel cylinder extraction algorithm applied to detected cylinders.

Main Results:

The proposed algorithm demonstrates superior performance in normal vector estimation compared to baseline methods.
Enhanced accuracy in detecting multiple cylinders simultaneously.
Improved cylinder extraction capabilities, outperforming existing approaches.

Conclusions:

The developed method offers a robust and comprehensive solution for cylinder detection and extraction.
The algorithm advances the field of 3D computer vision by addressing key challenges in geometric primitive extraction.
The findings provide a significant improvement for applications requiring accurate 3D object recognition.