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

A Robust Statistics Approach for Plane 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 107115, pp. 1-12, April 2020. [DOI]

Plane detection is a key component for many applications, such as industrial reverse engineering and self-driving cars. However, existing plane-detection techniques are sensitive to noise and to user-defined parameters. We introduce a fast deterministic technique for plane detection in unorganized point clouds that is robust to noise and virtually independent of parameter tuning. It is based on a novel planarity test drawn from robust statistics and on a split and merge strategy. Its parameter values are automatically adjusted to fit the local distribution of samples in the input dataset, thus leading to good reconstruction of even small planar regions. We demonstrate the effectiveness of our solution on several real datasets, comparing its performance to state-of-art plane detection techniques, and showing that it achieves better accuracy, while still being one of the fastest.

Keywords

Plane Detection; Region Growing; Robust Statistics; Unorganized Point Clouds.

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. A Robust Statistics Approach for Plane Detection in Unorganized Point Clouds, Pattern Recognition, Volume 100, Article 107115, pp. 1-12, 2020.

BibTeX

@article{AraujoOliveira_2020a,
    author  = {Abner M. C. Araujo and Manuel M. Oliveira},
    title   = {A Robust Statistics Approach for Plane Detection in Unorganized Point Clouds},
    journal = {Pattern Recognition},
    volume  = {100},
    note    = {Article 107115},
    DOI     = {10.1016/j.patcog.2019.107115},
    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.