Abstract
Estimating the pose of objects from range data is a problem of considerable practical importance for many vision applications. This paper presents an approach for accurate and efficient 3D pose estimation from 2.5D range images. Initialized with an approximate pose estimate, the proposed approach refines it so that it accurately accounts for an acquired range image. This is achieved by using a hypothesize-and-test scheme that combines Particle Swarm Optimization (PSO) and graphics-based rendering to minimize a cost function of object pose that quantifies the misalignment between the acquired and a hypothesized, rendered range image. Extensive experimental results demonstrate the superior performance of the approach compared to the Iterative Closest Point (ICP) algorithm that is commonly used for pose refinement.
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Acknowledgements
This work has received funding from the EC FP7 programme under grant no. 270138 DARWIN and by FORTH-ICS internal RTD Programme “Ambient Intelligence and Smart Environments”.
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Zabulis, X., Lourakis, M., Koutlemanis, P. (2015). 3D Object Pose Refinement in Range Images. In: Nalpantidis, L., Krüger, V., Eklundh, JO., Gasteratos, A. (eds) Computer Vision Systems. ICVS 2015. Lecture Notes in Computer Science(), vol 9163. Springer, Cham. https://doi.org/10.1007/978-3-319-20904-3_25
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