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 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.
Paper
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Document
PDF
Poster
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Bibtex
@InProceedings{Knopp10,
Author = "Knopp, J. and Prasad, M. and Willems,
G. and Timofte, R. and Van Gool, L.",
Title = "Hough Transform and 3D SURF for robust
three dimensional classification",
Booktitle = "Proceedings of the European
Conference on Computer Vision",
Year = 2010}
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Data
Codes
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We provide code of 3D SURF for
research use. Follow the link for code and how to use it.
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Related Documents
Orientation invariant 3D object Classification using Hough Transform based methods.
J. Knopp, M. Prasad and L. Van Gool
In ACM Multimedia WS on 3D Object Retrieval, Firenze, 2010
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paper (PDF)
