Rusu, R.B., Marton, Z.C., Blodow, N. and Beetz, M. (2008) Learning Informative Point Classes for the Acquisition of Object Model Maps. International Conference on Control, Automation, Robotics and Vision (ICARCV), December 2008, 643-650.

has been cited by the following article:

• TITLE: Experimental Evaluation of the Performance of Local Shape Descriptors for the Classification of 3D Data in Precision Farming

  AUTHORS: Jennifer Mack, Anatina Trakowski, Florian Rist, Katja Herzog, Reinhard Töpfer, Volker Steinhage

  KEYWORDS: Descriptor Performance, Precision Farming, 3D Data

  JOURNAL NAME: Journal of Computer and Communications, Vol.5 No.12, September 30, 2017

  ABSTRACT: Object classification in high-density 3D point clouds with applications in precision farming is a very challenging area due to high intra-class variances and high degrees of occlusions and overlaps due to self-similarities and densely packed plant organs, especially in ripe growing stages. Due to these application specific challenges, this contribution gives an experimental evaluation of the performance of local shape descriptors (namely Point-Feature Histogram (PFH), Fast-Point-Feature Histogram (FPFH), Signature of Histograms of Orientations (SHOT), Rotational Projection Statistics (RoPS) and Spin Images) in the classification of 3D points into different types of plant organs. We achieve very good results on four representative scans of a leave, a grape bunch, a grape branch and a flower of between 94 and 99% accuracy in the case of supervised classification with an SVM and between 88 and 96% accuracy using a k-means clustering approach. Additionally, different distance measures and the influence of the number of cluster centres are examined.