Volume 53, Number 2, April-June 2019
|Page(s)||487 - 504|
|Published online||01 May 2019|
An efficient simplification method for point cloud based on salient regions detection
Normandie Univ., UNIHAVRE, LMAH, FR CNRS 3335, ISCN, 76600 Le Havre, France
2 Beirut Arab university, Tripoli, Lebanon
3 Business and Administration Faculty, Lebanese university, Tripoli, Lebanon
4 Normandie Université, UNIHAVRE, ISEL, 76600 Le Havre, France
* Corresponding author: firstname.lastname@example.org
Accepted: 19 September 2018
Many computer vision approaches for point clouds processing consider 3D simplification as an important preprocessing phase. On the other hand, the big amount of point cloud data that describe a 3D object require excessively a large storage and long processing time. In this paper, we present an efficient simplification method for 3D point clouds using weighted graphs representation that optimizes the point clouds and maintain the characteristics of the initial data. This method detects the features regions that describe the geometry of the surface. These features regions are detected using the saliency degree of vertices. Then, we define features points in each feature region and remove redundant vertices. Finally, we will show the robustness of our method via different experimental results. Moreover, we will study the stability of our method according to noise.
Mathematics Subject Classification: 46N10 / 62H35 / 65D18
Key words: 3D point clouds simplification / 3D point clouds segmentation / 3D object processing / points clouds / saliency
© EDP Sciences, ROADEF, SMAI 2019
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