PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

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 2009.


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.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:5495
Deposited By:Michael Breitenstein
Deposited On:03 December 2009