Reconstructing feature-based CAD models based on point cloud morphology

Abstract

Reverse engineering, the process of obtaining a geometric CAD model from measurements obtained by scanning an existing physical model, is widely used in numerous applications, such as manufacturing, industrial design and jewellery design. In this work we propose a framework for reverse engineering objects of mechanical or freeform design to obtain fully editable feature-based CAD model that can be reproduced or modified before production. We focus on the process of detecting features on a point cloud and we present efficient methods for analyzing the morphology of the surface defined by the point cloud. We compute a point wise characteristic called point concavity intensity and we use this quantity along with the variations in the surface normal to detect regions corresponding to object features. The boundaries of the regions representing features are extracted and approximated by a collection of piecewise cubic rational Bezier curves that best fit the detected border point cloud and a ...
show more

All items in National Archive of Phd theses are protected by copyright.

DOI
10.12681/eadd/17717
Handle URL
http://hdl.handle.net/10442/hedi/17717
ND
17717
Alternative title
Ανακατασκευή μοντέλων CAD με χαρακτηριστικά βασισμένη στη μορφολογία του νέφους σημείων
Author
Stamati, Vassiliki (Father's name: Konstantinos)
Date
2008
Degree Grantor
University of Ioannina
Committee members
Φούντος Ιωάννης
Σαπίδης Νικόλαος
Θεοχάρης Θεοχάρης
Αζαριάδης Φίλιππος
Τραχανιάς Παναγιώτης
Δημακόπουλος Βασίλειος
Λάγαρης Ισαάκ
Discipline
Natural Sciences
Computer and Information Sciences
Keywords
Reverse engineering; Feature based CAD models; Point cloud segmentation; Curve fitting; Feature boundary reconstruction; Region growing; Rational bezier curves
Country
Greece
Language
English
Description
103 σ., im., ind.
Usage statistics
VIEWS
Concern the unique Ph.D. Thesis' views for the period 07/2018 - 07/2023.
Source: Google Analytics.
ONLINE READER
Concern the online reader's opening for the period 07/2018 - 07/2023.
Source: Google Analytics.
DOWNLOADS
Concern all downloads of this Ph.D. Thesis' digital file.
Source: National Archive of Ph.D. Theses.
USERS
Concern all registered users of National Archive of Ph.D. Theses who have interacted with this Ph.D. Thesis. Mostly, it concerns downloads.
Source: National Archive of Ph.D. Theses.
Related items (based on users' visits)