Abstract:
In this paper, we introduce a flexible framework for the reconstruction of a
surface from an unorganized point set, extending the geometric convection
approach introduced by Chaine [1]. Given a dense input point
cloud, we first extract a triangulated surface that interpolates a
subset of the initial data. We compute this surface in an output
sensitive manner by decimating the input point set on-the-fly during the
reconstruction process. Our simplification criterion relies on a simple procedure
that locally detects and reduces oversampling. If needed, we then operate in a
dynamic fashion for further local simplification by removing more points, or for
local refinement of the reconstructed surface by reinserting some eliminated
ones. Our method allows to locally update the reconstructed surface by inserting
or removing sample points without restarting the convection process from
scratch. This iterative correction process can be controlled interactively by
the user or automatized given some specific local sampling constraints.
Keywords: surface reconstruction, point cloud simplification, geometric convection, dynamic correction. [1] R. Chaine. A geometric convection approach of 3-D reconstruction. In Proc. Symposium on Geometry Processing, pages 218-229, 2003. |
Paper (PDF):
BibTeX entry:
@inproceedings{ allegre-smi05,
author = "R\'emi All\`egre and Rapha\"{e}lle Chaine and Samir Akkouche",
title = "Convection-Driven Dynamic Surface Reconstruction",
booktitle = "Proc. of Shape Modeling International",
pages = "33--42",
month = "June 15--17",
year = "2005",
address = "Cambridge, MA, USA"
}
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