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.