An efficient simplification method for point cloud based on salient regions detection - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue RAIRO - Operations Research Année : 2019

An efficient simplification method for point cloud based on salient regions detection

Résumé

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.

Dates et versions

hal-02304491 , version 1 (03-10-2019)

Identifiants

Citer

Abdul Rahman El Sayed, Abdallah El Chakik, Hassan Alabboud, Adnan Yassine. An efficient simplification method for point cloud based on salient regions detection. RAIRO - Operations Research, 2019, 53 (2), pp.487--504. ⟨10.1051/ro/2018082⟩. ⟨hal-02304491⟩
43 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More