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Hough Transform and 3D SURF for robust three dimensional classification

Jan Knopp and Mukta Prasad and Geert Willems and Radu Timofte and Luc Van Gool
Proceedings of the European Conference on Computer Vision
Heraklion, Greece, September 2010


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 keep- ing 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 trans- form 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.

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  author = {Jan Knopp and Mukta Prasad and Geert Willems and Radu Timofte and Luc Van Gool},
  title = {Hough Transform and 3D SURF for robust three dimensional classification},
  booktitle = {Proceedings of the European Conference on Computer Vision},
  year = {2010},
  month = {September},
  pages = {589-602},
  editor = {Kostas Daniilidis and Petros Maragos and Nikos Paragios},
  series = {Lecture Notes in Computer Science},
  publisher = {Springer},
  keywords = {Hough Transform 3D SURF 3D classification localization recognition }