PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Automatic Pose Estimation for Range Images on the GPU
Marcel Germann, Michael D. Breitenstein, In Kyu Park and Hanspeter Pfister
In: Int. Conference on 3D Digital Imaging and Modeling (3DIM'07), Montreal(2007).


Object pose (location and orientation) estimation is a common task in many computer vision applications. Although many methods exist, most algorithms need manual initialization and lack robustness to illumination variation, appearance change, and partial occlusions. We propose a fast method for automatic pose estimation without manual initialization based on shape matching of a 3D model to a range image of the scene. We developed a new error function to compare the input range image to pre-computed range maps of the 3D model. We use the tremendous dataparallel processing performance of modern graphics hardware to evaluate and minimize the error function on many range images in parallel. Our algorithm is simple and accurately estimates the pose of partially occluded objects in cluttered scenes in about one second.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:5496
Deposited By:Michael Breitenstein
Deposited On:03 December 2009