langbein.org: Constructing Regularity Feature Trees for Solid Models

Contents

M. Li, F. C. Langbein, R. R. Martin.

In: M.-S. Kim, K. Shimada (eds), Proc. Geometric Modeling and Processing, Springer LNCS, 4077:267-286, 2006.
ISBN 354036711X.

[DOI: 10.1007/11802914_19] [Preprint] [CiteSeer]

Approximate geometric models, e.g. as created by reverse engineering, describe the approximate shape of an object, but do not record the underlying design intent. Automatically inferring geometric aspects of the design intent, represented by feature trees and geometric constraints, enhances the utility of such models for downstream tasks. One approach to design intent detection in such models is to decompose them into regularity features. Geometric regularities such as symmetries may then be sought in each regularity feature, and subsequently be combined into a global, consistent description of the model's geometric design intent. This paper describes a systematic approach for finding such regularity features based on recovering broken symmetries in the model. The output is a tree of regularity features for subsequent use in regularity detection and selection. Experimental results are given to demonstrate the operation and efficiency of the algorithm.

More Examples

One approach to design intent detection in approximate geometric models is to decompose them into regularity features. Geometric regularities such as symmetries may then be sought in each regularity feature, and subsequently be combined into a global, consistent description of the model's geometric design intent. Regularity feature tree decomposition is systematic approach for finding such regularity features based on recovering broken symmetries in the model. The resulting tree may be used subsequently in regularity detection and selection. Below are some examples of regularity feature tree (RFT) decompositions of geometric models for design intent detection. Click on the images to see the detailed decomposition results for each model. The RFT decomposition algorithm has been implemented under Linux using OpenCASCADE and experiments were run on a 3.4GHz Pentium 4E with 1GB RAM.


Monster	2928056

Team Part	VS TP

Clevis 22	MOhne

ISO14649

These models were derived from models available at the National Design Repository at Drexel University.

@INPROCEEDINGS{Li2006,
  author =       {Ming Li and Frank C. Langbein and Ralph R. Martin},
  title =        {Constructing Regularity Feature Trees for Solid
                  Models},
  booktitle =    {Proc. Geometric Modeling and Processing},
  year =         2006,
  editor =       {M.-S. Kim and K. Shimada},
  volume =       4077,
  series =       {LNCS},
  pages =        {267-286},
  publisher =    {Springer},
  isbn =         {354036711X},
  doi =          {10.1007/11802914_19},
  url =          {http://www.langbein.org/research/solids/did/li2006/},
  abstract =     {Approximate geometric models, e.g. as created by
                  reverse engineering, describe the approximate shape
                  of an object, but do not record the underlying
                  design intent. Automatically inferring geometric
                  aspects of the design intent, represented by feature
                  trees and geometric constraints, enhances the
                  utility of such models for downstream tasks. One
                  approach to design intent detection in such models
                  is to decompose them into regularity features.
                  Geometric regularities such as symmetries may then
                  be sought in each regularity feature, and
                  subsequently be combined into a global, consistent
                  description of the model�s geometric design intent.
                  This paper describes a systematic approach for
                  finding such regularity features based on recovering
                  broken symmetries in the model. The output is a tree
                  of regularity features for subsequent use in
                  regularity detection and selection. Experimental
                  results are given to demonstrate the operation and
                  efficiency of the algorithm.},
}

Cite as Constructing Regularity Feature Trees for Solid Models, http://www.langbein.org/research/solids/did/li2006 by Frank C Langbein [ 6/December/2008, 19:21].

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