3D Object Pose Refinement in Range Images

  • Xenophon Zabulis
  • Manolis Lourakis
  • Panagiotis Koutlemanis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9163)


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.


Particle Swarm Optimization Depth Image Range Image Iterative Close Point Iterative Close Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



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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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Xenophon Zabulis
    • 1
  • Manolis Lourakis
    • 1
  • Panagiotis Koutlemanis
    • 1
  1. 1.Institute of Computer ScienceFoundation for Research and Technology - HellasHeraklionGreece

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