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Hough Transform and 3D SURF for Robust Three Dimensional Classification

  • Jan Knopp
  • Mukta Prasad
  • Geert Willems
  • Radu Timofte
  • Luc Van Gool
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6316)

Abstract

Most methods for the recognition of shape classes from 3D datasets focus on classifying clean, often manually generated models. However, 3D shapes obtained through acquisition techniques such as Structure-from-Motion or LIDAR scanning are noisy, clutter and holes. In that case global shape features—still dominating the 3D shape class recognition literature—are less appropriate. Inspired by 2D methods, recently researchers have started to work with local features. In keeping with this strand, we propose a new robust 3D shape classification method. It contains two main contributions. First, we extend a robust 2D feature descriptor, SURF, to be used in the context of 3D shapes. Second, we show how 3D shape class recognition can be improved by probabilistic Hough transform based methods, already popular in 2D. Through our experiments on partial shape retrieval, we show the power of the proposed 3D features. Their combination with the Hough transform yields superior results for class recognition on standard datasets. The potential for the applicability of such a method in classifying 3D obtained from Structure-from-Motion methods is promising, as we show in some initial experiments.

Keywords

Visual Word Interest Point Hough Transform Shape Retrieval Visual Vocabulary 
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 2010

Authors and Affiliations

  • Jan Knopp
    • 1
  • Mukta Prasad
    • 2
  • Geert Willems
    • 1
  • Radu Timofte
    • 1
  • Luc Van Gool
    • 1
    • 2
  1. 1.KU Leuven 
  2. 2.ETH Zurich 

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