Examples of automatic cylinder detection in unorganized point clouds obtained by our technique with comparisons against ground truth.

Connectivity-based Cylinder Detection in Unorganized Point Clouds

Abner M. C. Araújo abnerrjo@gmail.com

and

Manuel M. Oliveira oliveira@inf.ufrgs.br

Pattern Recognition.
Volume 100, Article 107161, pp. 1-12, April 2020. [DOI]

Cylinder detection is an important step in reverse engineering of industrial sites, as such environments often contain a large number of cylindrical pipes and tanks. However, existing techniques for cylinder detection require the specification of several parameters which are difficult to adjust because their values depend on the noise level of the input point cloud. Also, these solutions often expect the cylinders to be either parallel or perpendicular to the ground. We present a cylinder-detection technique that is robust to noise, contains parameters which require little to no fine-tuning, and can handle cylinders with arbitrary orientations. Our approach is based on a robust linear-time circle-detection algorithm that naturally discards outliers, allowing our technique to handle datasets with various density and noise levels while using a set of default parameter values. It works by projecting the point cloud onto a set of directions over the unit hemisphere and detecting circular projections formed by samples defining connected components in 3D. The extracted cylindrical surfaces are obtained by fitting a cylinder to each connected component. We compared our technique against the state-of-the-art methods on both synthetic and real datasets containing various densities and noise levels, and show that it outperforms existing techniques in terms of accuracy and robustness to noise, while still maintaining a competitive running time.

Keywords

Cylinder Detection, Unorganized Point Clouds; Reverse Engineering; Industrial sites.

Paper

Paper (pre-print)

The final publication (incorporating the feedback from the reviewing process) is available at ScienceDirect - Elsevier

Code

Code

Data

Datasets

Citation

Abner M. C. Araújo, Manuel M. Oliveira. Connectivity-based Cylinder Detection in Unorganized Point Clouds, Pattern Recognition, Volume 100, Article 107161, pp. 1-12, 2020.

BibTeX

@article{AraujoOliveira_2020b,
    author  = {Abner M. C. Araujo and Manuel M. Oliveira},
    title   = {Connectivity-based Cylinder Detection in Unorganized Point Clouds},
    journal = {Pattern Recognition},
    volume  = {100},
    note    = {Article 107161},
    DOI     = {10.1016/j.patcog.2019.107161},
    ISSN    = {0031-3203},
    pages   = {1--12},
    year    = {2020}
}
  

This work was sponsored by

	CNPq-Brazil fellowships and grants 130895/2017-2, 312975/2018-0, 423673/2016-5.
	ONR Global Award # N62909-18-1-2131.