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A Dynamic Surface Reconstruction Framework for Large Unstructured Point Sets
 

Summary: A Dynamic Surface Reconstruction Framework
for Large Unstructured Point Sets
R´emi All`egre, Rapha¨elle Chaine and Samir Akkouche
LIRIS CNRS / Universit´e Lyon 1, Villeurbanne, France
Abstract
We present a method to reconstruct simplified mesh
surfaces from large unstructured point sets, extending
recent work on dynamic surface reconstruction. The
method consists of two core components: an efficient
selective reconstruction algorithm, based on geometric
convection, that simplifies the input point set while re-
constructing a surface, and a local update algorithm that
dynamically refines or coarsens the reconstructed sur-
face according to specific local sampling constraints.
We introduce a new data-structure that significantly
accelerates the original selective reconstruction algo-
rithm and makes it possible to handle point set models
with millions of sample points. Our data-structure
mixes a kd-tree with the Delaunay triangulation of
the selected points enriched with a sparse subset

  

Source: Allègre, Rémi - Département Informatique, Université de Strasbourg
Chaine, Raphaëlle - Laboratoire d'InfoRmatique en Image et Systèmes d'information, Université Claude Bernard (Lyon I)

 

Collections: Computer Technologies and Information Sciences