Please use this identifier to cite or link to this item:
|Title:||3D CAD model search: A regularized manifold learning approach|
|Authors:||Zhu, K.P. |
|Source:||Zhu, K.P., Wong, Y.S., Loh, H.T., Lu, W.F., Fuh, J.Y.H. (2009). 3D CAD model search: A regularized manifold learning approach. 2009 IEEE International Conference on Robotics and Biomimetics, ROBIO 2009 : 639-644. ScholarBank@NUS Repository. https://doi.org/10.1109/ROBIO.2009.5420597|
|Abstract:||3D model matching has been widely studied in computer vision, graphics and robotics. While there is much success made in the matching of natural objects, most of these approaches consider smooth surfaces and are not suitable for computer aided design (CAD) models because of their complex topology and singular structures. This paper presents a novel spectral approach for the 3D CAD model matching in the framework of manifold learning. The 3D models are treated as undirected graphs. A regularized Laplacian spectrum approach is applied to solve this problem where the regularization term is used to characterize the shape geometries. Spectral distributions of different models are obtained and then compared by their divergence for model retrieval. The proposed approach is tested with models from known 3D CAD database for verification. © 2009 IEEE.|
|Source Title:||2009 IEEE International Conference on Robotics and Biomimetics, ROBIO 2009|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 10, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.