Efficient Recognition of Highly Similar 3D Objects in Range Images

Hui Chen; B. Bhanu

doi:10.1109/tpami.2008.176

Efficient Recognition of Highly Similar 3D Objects in Range Images

IEEE Transactions on Pattern Analysis and Machine Intelligence ◽

10.1109/tpami.2008.176 ◽

2009 ◽

Vol 31 (1) ◽

pp. 172-179 ◽

Cited By ~ 47

Author(s):

Hui Chen ◽

B. Bhanu

Keyword(s):

Range Images ◽

3D Objects ◽

Efficient Recognition

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Geometrical cloning of 3D objects via simultaneous registration of multiple range images

Proceedings of 1997 International Conference on Shape Modeling and Applications ◽

10.1109/sma.1997.634890 ◽

2002 ◽

Cited By ~ 55

Author(s):

P.J. Neugebauer

Keyword(s):

Range Images ◽

3D Objects ◽

Simultaneous Registration

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Feature correspondence and localization of 3D objects using range images

1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century ◽

10.1109/icsmc.1995.538338 ◽

2002 ◽

Author(s):

S. Kaveti ◽

Eam Khwang Teoh ◽

Han Wang

Keyword(s):

Range Images ◽

Feature Correspondence ◽

3D Objects

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Consensus Surfaces for Modeling 3D Objects from Multiple Range Images

Modeling from Reality ◽

10.1007/978-1-4615-0797-0_3 ◽

2001 ◽

pp. 77-92

Author(s):

Mark D. Wheeler ◽

Yoichi Sato ◽

Katsushi Ikeuchi

Keyword(s):

Range Images ◽

3D Objects

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CAD-based inspection of 3D objects using range images

Proceedings of 1994 IEEE 2nd CAD-Based Vision Workshop ◽

10.1109/cadvis.1994.284496 ◽

2002 ◽

Cited By ~ 2

Author(s):

T.S. Newman ◽

A.K. Jain

Keyword(s):

Range Images ◽

3D Objects

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COIFLET BASED METHODS FOR RANGE IMAGE PROCESSING

International Journal of Image and Graphics ◽

10.1142/s0219467807002672 ◽

2007 ◽

Vol 07 (02) ◽

pp. 321-351

Author(s):

M. DJEBALI ◽

M. MELKEMI ◽

K. MELKEMI ◽

N. SAPIDIS

Keyword(s):

Image Processing ◽

Reconstruction Algorithm ◽

Geometrical Model ◽

Range Data ◽

Range Image ◽

Range Images ◽

3D Objects ◽

Industry Applications ◽

Cad Cam ◽

Range Image Processing

In industry applications, the range images are generally huge points arrays and are additively noised. They usually represent surfaces of 3D objects and are used for reverse engineering process in CAD/CAM domains. To compute the geometrical model of each surface present in the range image, we denoise and sub-sample the raw range data. Denoising allows us to avoid the adverse effects of the noise on the obtained result. Sub-sampling the raw range data leads to a low image processing overheads like those of segmentation process. Based on interpolation properties of particular wavelets named coiflets, we propose a method for smoothing noisy range images. The smoothed image keeps invariant the "topological characteristics" of the represented surfaces. Thereafter, we propose a method for sub-sampling dense range images which leads to the reduction of the amount of raw data by a factor of four. This method eliminates the "redundant" information, thus the obtained result describes the essential details (as the shape of the physical surface) of the initial range image. The smoothing and sub-sampling methods are designed to be easily integrated in any reconstruction algorithm to improve its result and reduce its overhead in spite of its high complexity.

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