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Fast and Automatic Object Pose Estimation for Range Images on the GPU

In Kyu Park, Marcel Germann, Michael D. Breitenstein, and Hanspeter Pfister
Machine Vision and Applications
August 2009

Abstract

We present a pose estimation method for rigid objects from single range images. Using 3D models of the objects, many pose hypotheses are compared in a data-parallel version of the downhill simplex algorithm with an image-based error function. The pose hypothesis with the lowest error value yields the pose estimation (location and orientation), which is refined using ICP. The algorithm is designed especially for implementation on the GPU. It is completely automatic, fast, robust to occlusion and cluttered scenes, and scales with the number of different object types. We apply the system to bin picking, and evaluate it on cluttered scenes. Comprehensive experiments on challenging synthetic and real-world data demonstrate the effectiveness of our method.


Link to publisher's page
@Article{eth_biwi_00628,
  author = {In Kyu Park and Marcel Germann and Michael D. Breitenstein and and Hanspeter Pfister},
  title = {Fast and Automatic Object Pose Estimation for Range Images on the GPU},
  journal = {Machine Vision and Applications},
  year = {2009},
  month = {August},
  pages = {},
  volume = {},
  number = {},
  keywords = {}
}