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A Unified Energy Minimization Framework for Model Fitting in Depth

  • Carl Yuheng Ren
  • Ian Reid
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7584)

Abstract

In this paper we present a unified energy minimization framework for model fitting and pose recovery problems in depth cameras. 3D level-set embedding functions are used to represent object models implicitly and a novel 3D chamfer matching based energy function is minimized by adjusting the generic projection matrix, which could be parameterized differently according to specific applications. Our proposed energy function takes the advantage of the gradient of 3D level-set embedding function and can be efficiently solved by gradients-based optimization methods. We show various real-world applications, including real-time 3D tracking in depth, simultaneous calibration and tracking, and 3D point cloud modeling. We perform experiments on both real data and synthetic data to show the superior performance of our method for all the applications above.

Keywords

Point Cloud Energy Function Depth Image Intrinsic Parameter Depth Camera 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Carl Yuheng Ren
    • 1
  • Ian Reid
    • 1
  1. 1.Dept. Engineering ScienceOxford UniversityOxfordUK

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