Authors:
Viktor Seib
;
Nick Theisen
and
Dietrich Paulus
Affiliation:
Active Vision Group (AGAS), University of Koblenz-Landau, Universitätsstr. 1, 56070 Koblenz and Germany
Keyword(s):
Shape Classification, Global Verification, Mobile Robotics, Implicit Shape Models, Point Clouds, Codebooks.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Pattern Recognition
;
Robotics
;
Software Engineering
Abstract:
We present a competitive approach for 3D data classification that is related to Implicit Shape Models and Naive-Bayes Nearest Neighbor algorithms. Based on this approach we investigate methods to reduce the amount of data stored in the extracted codebook with the goal to eliminate redundant and ambiguous feature descriptors. The codebook is significantly reduced in size and is combined with a novel global verification approach. We evaluate our algorithms on typical 3D data benchmarks and achieve competitive results despite the reduced codebook. The presented algorithm can be run efficiently on a mobile computer making it suitable for mobile robotics applications. The source code of the developed methods is made publicly available to contribute to point cloud processing, the Point Cloud Library (PCL) and 3D classification software in general.
