Computer Science ›› 2019, Vol. 46 ›› Issue (2): 266-270.doi: 10.11896/j.issn.1002-137X.2019.02.041

• Graphics ,Image & Pattern Recognition • Previous Articles     Next Articles

FPFH Feature Extraction Algorithm Based on Adaptive Neighborhood Selection

WU Fei, ZHAO Xin-can, ZHAN Peng-lei, GUAN Ling   

  1. School of Information Engineering,Zhengzhou University,Zhengzhou 450001,China
  • Received:2018-01-24 Online:2019-02-25 Published:2019-02-25

Abstract: When using the FPFH feature of point cloud for 3D object recognition or registration,FPFH feature descriptor is arbitrarily and inefficiently calculated by subjectively adjusting the neighborhood radius,and the whole process can not be completed automatically.This paper proposed an adaptive neighborhood-selection FPFH point cloud feature extraction algorithm to solve this problem.Firstly,the point cloud densities of many pairs of point clouds were estimated.Secondly,the neighborhood radii were computed to extract the FPFH features for SAC-IA,and the radii and the densities were counted when the registration performance is the most optimal,and then the Cubic Spline Interpolation Fitting was used to fit the function expression of the radii and the densities to form the adaptive neighborhood-selection FPFH feature extraction algorithm.The experimental results show that this algorithm can adaptively choose the appropriate neighborhood radius according to the density of point cloud,improves the FPFH feature matching perfor-mance,and improves the computing speed at the same time,indicating that the proposed algorithm is of important guiding significance.

Key words: Fast point feature histograms, Neighborhood radius, Point cloud density, Sample consensus initial alignment

CLC Number: 

  • TP391
[1]RUSU R B,MARTON Z C,BLODOW N,et al.Persistent point feature histograms for 3D point clouds[C]∥Proc 10th Int Conf. Intel Autonomous Syst (IAS-10).Baden-Baden,Germany:IOS Press,2008:119-128.
[2]RUSU R B,MARTON Z C,BLODOW N,et al.Learning informative point classes for the acquisition of object model maps[C]∥10th International Conference on Control,Automation,Robotics and Vision,2008(ICARCV 2008).IEEE,2008:643-650.
[3]RUSU R B,BLODOW N,MARTON Z C,et al.Aligning point cloud views using persistent feature histograms[C]∥IEEE/RSJ International Conference on Intelligent Robots and Systems,2008(IROS 2008).IEEE,2008:3384-3391.
[4]RUSU R B,BLODOW N,BEETZ M.Fast point feature histograms (FPFH) for 3D registration[C]∥IEEE International Conference on Robotics and Automation,2009(ICRA’09).IEEE,2009:3212-3217.
[5]TAO Y,ZHOU J.Automatic apple recognition based on the fusion of color and 3D feature for robotic fruit picking[J].Computers and Electronics in Agriculture,2017,142:388-396.
[6]HUANG J,YOU S.Detecting objects in scene point cloud:A combinational approach[C]∥2013 International Conference on 3D Vision-3DV 2013.IEEE,2013:175-182.
[7]NASAB S E,KASAEI S,SANAEI E,et al.Multiview 3D reconstruction and human point cloud classification[C]∥22nd Iranian Conference on Electrical Engineering (ICEE).IEEE,2014:1119-1124.
[8]SHEN B,YIN F,CHOU W.A 3D Modeling Method of Indoor Objects Using Kinect Sensor[C]∥2017 10th International Symposium on Computational Intelligence and Design (ISCID).IEEE,2017:64-68.
[9]YE A F,GONG S R,WANG C H,et al.Point Cloud Density Extraction Based on Stochastic Distribution Estimation[J].Computer Engineering,2009,35(4):183-186.(in Chinese)
叶爱芬,龚声蓉,王朝晖,等.基于随机分布估计的点云密度提取[J].计算机工程,2009,35(4):183-186.
[10]SADIKIN R,SWARDIANA I W A,WIRAHMAN T.Cubic spline interpolation for large regular 3D grid in cylindrical coordinate[C]∥International Conference on Computer,Control,Informatics and Its Applications (IC3INA).IEEE,2017:1-6.
[11]REVESZ P Z.A recurrenceequation-based solution for the cubic spline interpolation problem[J].International Journal of Mathematical Models and Methods in Applied Sciences,2015,9(1):446-452.
[12]CHENG L,CHEN H,YU Q,et al.Research on bionic olfactory temperature compensation mechanism[C]∥2017 2nd International Conference on Advanced Robotics and Mechatronics(ICARM).IEEE,2017:316-321.
[1] CHEN Zhi-qiang, HAN Meng, LI Mu-hang, WU Hong-xin, ZHANG Xi-long. Survey of Concept Drift Handling Methods in Data Streams [J]. Computer Science, 2022, 49(9): 14-32.
[2] WANG Ming, WU Wen-fang, WANG Da-ling, FENG Shi, ZHANG Yi-fei. Generative Link Tree:A Counterfactual Explanation Generation Approach with High Data Fidelity [J]. Computer Science, 2022, 49(9): 33-40.
[3] ZHANG Jia, DONG Shou-bin. Cross-domain Recommendation Based on Review Aspect-level User Preference Transfer [J]. Computer Science, 2022, 49(9): 41-47.
[4] ZHOU Fang-quan, CHENG Wei-qing. Sequence Recommendation Based on Global Enhanced Graph Neural Network [J]. Computer Science, 2022, 49(9): 55-63.
[5] SONG Jie, LIANG Mei-yu, XUE Zhe, DU Jun-ping, KOU Fei-fei. Scientific Paper Heterogeneous Graph Node Representation Learning Method Based onUnsupervised Clustering Level [J]. Computer Science, 2022, 49(9): 64-69.
[6] CHAI Hui-min, ZHANG Yong, FANG Min. Aerial Target Grouping Method Based on Feature Similarity Clustering [J]. Computer Science, 2022, 49(9): 70-75.
[7] ZHENG Wen-ping, LIU Mei-lin, YANG Gui. Community Detection Algorithm Based on Node Stability and Neighbor Similarity [J]. Computer Science, 2022, 49(9): 83-91.
[8] LYU Xiao-feng, ZHAO Shu-liang, GAO Heng-da, WU Yong-liang, ZHANG Bao-qi. Short Texts Feautre Enrichment Method Based on Heterogeneous Information Network [J]. Computer Science, 2022, 49(9): 92-100.
[9] XU Tian-hui, GUO Qiang, ZHANG Cai-ming. Time Series Data Anomaly Detection Based on Total Variation Ratio Separation Distance [J]. Computer Science, 2022, 49(9): 101-110.
[10] NIE Xiu-shan, PAN Jia-nan, TAN Zhi-fang, LIU Xin-fang, GUO Jie, YIN Yi-long. Overview of Natural Language Video Localization [J]. Computer Science, 2022, 49(9): 111-122.
[11] CAO Xiao-wen, LIANG Mei-yu, LU Kang-kang. Fine-grained Semantic Reasoning Based Cross-media Dual-way Adversarial Hashing Learning Model [J]. Computer Science, 2022, 49(9): 123-131.
[12] ZHOU Xu, QIAN Sheng-sheng, LI Zhang-ming, FANG Quan, XU Chang-sheng. Dual Variational Multi-modal Attention Network for Incomplete Social Event Classification [J]. Computer Science, 2022, 49(9): 132-138.
[13] DAI Yu, XU Lin-feng. Cross-image Text Reading Method Based on Text Line Matching [J]. Computer Science, 2022, 49(9): 139-145.
[14] QU Qian-wen, CHE Xiao-ping, QU Chen-xin, LI Jin-ru. Study on Information Perception Based User Presence in Virtual Reality [J]. Computer Science, 2022, 49(9): 146-154.
[15] ZHOU Le-yuan, ZHANG Jian-hua, YUAN Tian-tian, CHEN Sheng-yong. Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion [J]. Computer Science, 2022, 49(9): 155-161.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!