International journal of


EISSN: 2313-3724, Print ISSN:2313-626X

Frequency: 12

line decor
line decor

 Volume 4, Issue 11 (November 2017), Pages: 121-126


 Original Research Paper

 Title: A point cloud decomposition by the 3D level scanning for planes detection

 Author(s): Pavel Chmelar *, Lubos Rejfek, Ladislav Beran, Martin Dobrovolny


 Department of Electrical Engineering, Faculty of Electrical Engineering and Informatics, University of Pardubice, Pardubice, Czech Republic

 Full Text - PDF          XML


A point cloud represents a set of measurement points. Usually it is a group of points in a defined coordinate space without any information how individual points relates to each other. For a simple shapes and objects description additional methods are needed. In this paper we would like to present a new 3D point cloud scanning method for planes detection. Our developed algorithm includes several image processing methods like the connected component labeling and the shape borders detection which allows computing important plane properties end even detect object shapes. The scanning algorithm is described on a testing example and verified on real measured data. The paper concludes by algorithm properties summarization and recommendations where this method can be used. 

 © 2017 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (

 Keywords: Point cloud, Plane detection, Level scanning, Component labeling

 Article History: Received 12 February 2017, Received in revised form 18 September 2017, Accepted 19 September 2017

 Digital Object Identifier:


 Chmelar P, Rejfek L, Beran L, and Dobrovolny M (2017). A point cloud decomposition by the 3D level scanning for planes detection. International Journal of Advanced and Applied Sciences, 4(11): 121-126

 Permanent Link:


 References (11)

  1. Beran L, Chmelar P, and Rejfek L (2015). Navigation of robotics platform using monocular visual odometry. In the 25th International Conference on Radioelektronika (RADIOELEKTRONIKA), IEEE: 213-216. 
  2. Borrmann D, Elseberg J, Lingemann K, and Nüchter A (2011). The 3D Hough transform for plane detection in point clouds: A review and a new accumulator design. 3D Research, 2(2): 1-13.     
  3. Chmelar P and Dobrovolny M (2013). The fusion of ultrasonic and optical measurement devices for autonomous mapping. In the 23rd International Conference on Radioelektronika (RADIOELEKTRONIKA), IEEE: 292-296. 
  4. Chmelar P, Beran L, and Rejfek L (2016). The depth map construction from a 3D point cloud. MATEC Web of Conferences (ICMIE 2016), EDP Sciences, 75: 1-6. 
  5. Cupec R, Grbic R, Nyarko EK, Sabo K, and Scitovski R (2009). Detection of planar surfaces based on RANSAC and LAD plane fitting. In the 4th European Conference on Mobile Robots (ECMR'09), Dubrovnik, Croatia: 37-42.     
  6. Deschaud JE and Goulette F (2010). A fast and accurate plane detection algorithm for large noisy point clouds using filtered normals and voxel growing. In the Conference of 3D Processing, Visualization and Transmission Conference (3DPVT'10), Paris, France.     
  7. Dunn F and Parberry I (2011). 3D math primer for graphics and game development. CRC Press, Boca Raton, USA. 
  8. Fujiwara T, Kamegawa T, and Gofuku A (2013). Plane detection to improve 3D scanning speed using RANSAC algorithm. In the 8th IEEE Conference on Industrial Electronics and Applications (ICIEA'13), IEEE: 1863 1869. 
  9. Kurban R, Skuka F, and Bozpolat H (2015). Plane segmentation of kinect point clouds using RANSAC. In the 7th International Conference on Information Technology, Amman, Jordan: 545-551. 
  10. Palnick J (2014). Plane detection and segmentation for DARPA robotics challenge. Ph.D. Dissertation, Worcester Polytechnic Institute, Worcester, USA.     
  11. Schnabel R, Wahl R, and Klein R (2007). Efficient RANSAC for point‐cloud shape detection. Computer Graphics Forum, Blackwell Publishing Ltd, 26(2): 214-226.