Extracting surface patches from complete range descriptions

Fisher, Robert B.; Fitzgibbon, Andrew; Eggert, Daniel

doi:10.1109/im.1997.603860

Cited by 22 publications

(14 citation statements)

References 34 publications

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“…The main disadvantage is the larger computational cost that arises in parameter space search instead of parametric surface growing in data space, either in 2D [3] or 3D [18], or for triangulated 3D surfaces [25]. However, since reconstruction is usually a one-time process, the extra cost (e.g.…”

Section: Evolutionary Structure Recovery Parameter Space Search To £Nmentioning

confidence: 99%

Solving architectural modelling problems using knowledge

Fisher¹

Fourth International Conference on 3-D Digital Imaging and Modeling, 2003. 3DIM 2003. Proceedings.

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Section: Evolutionary Structure Recovery Parameter Space Search To £Nmentioning

confidence: 99%

Solving architectural modelling problems using knowledge

Fisher¹

Fourth International Conference on 3-D Digital Imaging and Modeling, 2003. 3DIM 2003. Proceedings.

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“…The advantages of evolutionary methods are: 1) Euclidean and robust error metrics are easily incorporated into the evaluation criteria and 2) initializing the optimization is not a big problem with the use of multiple "chromosomes" as the initial starting points. The main disadvantage is the larger computational cost that arises in parameter space search instead of parametric surface growing in data space, either in 2D [4] or 3D [19], or for triangulated 3D surfaces [27]. However, since reverse engineering a shape is usually a one-time process, the extra cost (e.g.…”

Section: Evolutionary Structure Recoverymentioning

confidence: 99%

Applying knowledge to reverse engineering problems

Fisher

2004

Computer-Aided Design

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:This paper summarizes a series of recent research results made at Edinburgh University based on projects that apply domain knowledge of standard shapes and relationships to solve or improve reverse engineering problems. The problems considered are how to enforce known relationships when data fitting, how to extract features even in very noisy data, how to get better shape parameter estimates and how to infer data about unseen features.Keywords : range data, reverse engineering, constraints Copyright c 2004 by The University of Edinburgh. All Rights ReservedThe authors and the University of Edinburgh retain the right to reproduce and publish this paper for non-commercial purposes.Permission is granted for this report to be reproduced by others for non-commercial purposes as long as this copyright notice is reprinted in full in any reproduction. Applications to make other use of the material should be addressed in the first instance to Copyright Permissions, School of Informatics, The University of Edinburgh, 2 Buccleuch Place, Edinburgh EH8 9LW, Scotland. DRAFT: Applying knowledge to reverse engineering problemsRobert B. Fisher School of Informatics, University of Edinburgh rbf@inf.ed.ac.uk AbstractThis paper summarizes a series of recent research results made at Edinburgh University based on projects that apply domain knowledge of standard shapes and relationships to solve or improve reverse engineering problems. The problems considered are how to enforce known relationships when data fitting, how to extract features even in very noisy data, how to get better shape parameter estimates and how to infer data about unseen features.

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“…The object was first segmented into individual surfaces using a region growing method and Taubin accumulation for the fitting [13]. Each of the different surfaces was saved as a 3d data set.…”

Section: A Real Objectmentioning

confidence: 99%

An Evolutionary Approach to Fitting Constrained Degenerate Second Order Surfaces

Robertson

Fisher

Werghi

et al. 1999

Evolutionary Image Analysis, Signal Processing and Telecommunications

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In this work we examine the applicability of an evolutionary strategy to the problem of fitting constrained second-order surfaces to both synthetic and acquired 3D data. In particular we concentrate on the Genocop III algorithm proposed by Michalewicz [8] for the optimization of constrained functions. This is a novel application of this algorithm which has demonstrably good results when applied using parametric models. Example times for convergence are given which compare the approach to standard techniques.

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Extracting surface patches from complete range descriptions

Abstract: Abstract

Cited by 22 publications

References 34 publications

Solving architectural modelling problems using knowledge

Solving architectural modelling problems using knowledge

Applying knowledge to reverse engineering problems

An Evolutionary Approach to Fitting Constrained Degenerate Second Order Surfaces

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