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Learning to Rank 3D Features

  • Oncel Tuzel
  • Ming-Yu Liu
  • Yuichi Taguchi
  • Arvind Raghunathan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8689)

Abstract

Representation of three dimensional objects using a set of oriented point pair features has been shown to be effective for object recognition and pose estimation. Combined with an efficient voting scheme on a generalized Hough space, existing approaches achieve good recognition accuracy and fast operation. However, the performance of these approaches degrades when the objects are (self-)similar or exhibit degeneracies, such as large planar surfaces which are very common in both man made and natural shapes, or due to heavy object and background clutter. We propose a max-margin learning framework to identify discriminative features on the surface of three dimensional objects. Our algorithm selects and ranks features according to their importance for the specified task, which leads to improved accuracy and reduced computational cost. In addition, we analyze various grouping and optimization strategies to learn the discriminative pair features. We present extensive synthetic and real experiments demonstrating the improved results.

Keywords

3D pose estimation feature selection max-margin learning 

Supplementary material

Electronic Supplementary Material (MOV 20,586 KB)

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Oncel Tuzel
    • 1
  • Ming-Yu Liu
    • 1
  • Yuichi Taguchi
    • 1
  • Arvind Raghunathan
    • 1
  1. 1.Mitsubishi Electric Research Labs (MERL)CambridgeUSA

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