Randomized Trees for Real-Time Keypoint Recognition

  • Authors:

  • Vincent Lepetit;Pascal Lagger;Pascal Fua

  • Affiliations:

  • École Polytechnique Fédérale de Lausanne;École Polytechnique Fédérale de Lausanne;École Polytechnique Fédérale de Lausanne

  • Venue:
  • CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
  • Year:
  • 2005

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Abstract

In earlier work, we proposed treating wide baseline matching of feature points as a classification problem, in which each class corresponds to the set of all possible views of such a point. We used a K-mean plus Nearest Neighbor classifier to validate our approach, mostly because it was simple to implement. It has proved effective but still too slow for real-time use. In this paper, we advocate instead the use of randomized trees as the classification technique. It is both fast enough for real-time performance and more robust. It also gives us a principled way not only to match keypoints but to select during a training phase those that are the most recognizable ones. This results in a real-time system able to detect and position in 3D planar, non-planar, and even deformable objects. It is robust to illuminations changes, scale changes and occlusions.