filename     : Park09.pdf
entry        : article
conference   :  
pages        : 749-766
year         : 2009
month        : August
title        : Fast and automatic object pose estimation for range images on the GPU
subtitle     :
author       : In Kyu Park, Marcel Germann, Michael D. Breitenstein, Hanspeter Pfister
booktitle    : Machine Vision and Applications
ISSN/ISBN    : 0932-8092 (Print) 1432-1769 (Online)
editor       : Helmut Petri
publisher    : Springer
publ.place   : Berlin / Heidelberg
volume       : 21
issue        : 5
language     : english 
keywords     : Object pose estimation - Bin picking - Range image processing - General purpose GPU programming - Iterative closest point - Euclidean distance transform - Downhill simplex - CUDA 
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.