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Comparison of Local Image Descriptors for Full 6 Degree-of-Freedom Pose Estimation

Fredrik Viksten, Per-Erik Forssén, Björn Johansson, Anders Moe
ICRA09, Kobe, Japan
IEEE International Conference on Robotics and Automation
May 2009


Recent years have seen advances in the estimation of full 6 degree-of-freedom object pose from a single 2D image. These advances have often been presented as a result of, or together with, a new local image descriptor. This paper examines how the performance for such a system varies with choice of local descriptor. This is done by comparing the performance of a full 6 degree-of-freedom pose estimation system for fourteen types of local descriptors. The evaluation is done on a database with photos of complex objects with simple and complex backgrounds and varying lighting conditions. From the experiments we can conclude that duplet features, that use pairs of interest points, improve pose estimation accuracy, and that affine covariant features do not work well in current pose estimation frameworks. The data sets and their ground truth is available on the web to allow future comparison with novel algorithms.

Full Paper

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The paper introduces a dataset that can be found here.

Bibtex entry

  author = 	 {Fredrik Viksten and Per-Erik Forss{\'e}n and Bj{\"o}rn Johansson and Anders Moe},
  title = 	 {Comparison of Local Image Descriptors for Full 6 Degree-of-Freedom Pose Estimation},
  booktitle = {{IEEE} International Conference on Robotics and Automation},
  year = 	 {2009},
  OPTvolume = 	 {},
  address = 	 {Kobe, Japan},
  month = 	 {May},
  organization = {IEEE},
  isbn =         {987-1-4244-2789-5},
  publisher =    {{IEEE} Robotics and Automation Society}

Per-Erik Forssén

Per-Erik Forssén


Computer Vision Laboratory
Department of Electrical Engineering
Building B
Room 2D:521
SE-581 83 Linköping, Sweden
+46(0)13 285654

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