A method of 3D object recognition and localization in a cloud of points
Jerzy Bielicki , Robert Sitnik
AbstractThe proposed method given in this article is prepared for analysis of data in the form of cloud of points directly from 3D measurements. It is designed for use in the end-user applications that can directly be integrated with 3D scanning software. The method utilizes locally calculated feature vectors (FVs) in point cloud data. Recognition is based on comparison of the analyzed scene with reference object library. A global descriptor in the form of a set of spatially distributed FVs is created for each reference model. During the detection process, correlation of subsets of reference FVs with FVs calculated in the scene is computed. Features utilized in the algorithm are based on parameters, which qualitatively estimate mean and Gaussian curvatures. Replacement of differentiation with averaging in the curvatures estimation makes the algorithm more resistant to discontinuities and poor quality of the input data. Utilization of the FV subsets allows to detect partially occluded and cluttered objects in the scene, while additional spatial information maintains false positive rate at a reasonably low level.
|Journal series||EURASIP Journal on Advances in Signal Processing, ISSN 1687-6172|
|Publication size in sheets||0.6|
|Keywords in English||3D object recognition, Point cloud|
|ASJC Classification||; ;|
|Score|| = 25.0, 19-06-2020, ArticleFromJournal|
= 25.0, 19-06-2020, ArticleFromJournal
|Publication indicators||= 3; = 2; = 11.0; : 2013 = 1.114; : 2013 = 0.808 (2) - 2013=1.015 (5)|
|Citation count*||11 (2020-05-31)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.