Convection-driven dynamic surface reconstruction

Abstract

Reconstruction of a simplified meshLocal dynamic reconstruction refinement Figure 1. Our reconstruction framework, illustrated on the TRIPLE HECATE model. Starting from a dense input point set, we reconstruct a simplified mesh (center). Benefiting from the connectivity of this initial reconstruction, we can make it to evolve dynamically so as to refine the approximation locally. This refinement can be achieved either in an automatic fashion, for example in order to improve the quality of the elements of the mesh, or interactively, in order to add or remove sample points. Here, the draped dress has been locally enhanced (right). AbstractIn 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 [9]. 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 procedure relies on a simple criterion that locally detects and reduces oversampling. If needed, we then operate in a dynamic fashion for local refinement or further simplification of the reconstructed surface. 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.

Survey on 3D Surface Reconstruction

“…The proposed data structure is a combination of kd-tree and Delaunay triangulation. An extension of this approach is proposed by Allegre et al [30].…”

Evolution of T-spline level sets for meshing non-uniformly sampled and incomplete data

“…Depending on the area of the application, different representations have been used, such as triangular meshes [3,7,15,38,2,41,47], subdivision surfaces [28,44,12], parametric spline surfaces [16,23], discretized level sets [42,57,8], scalar spline functions [45,33], radial basis functions [10,40] and point set surfaces [1,43,19]. It is beyond the scope of this paper to give a detailed overview of all the existing work.…”

Efficient Surface Reconstruction using Generalized Coulomb Potentials

“…The main drawback is the sensitivity to shot noise, due to reliance on the distance transform. The work of Zhao et al [31] inspired researchers from the computational-geometry community to develop 'geometric convection' algorithms [2,11] in the context of surface reconstruction. These methods deform a closed oriented pseudo-surface embedded in the 3D Delaunay triangulation of the sampled points, and yield the reconstructed surface as a set of oriented facets of the triangulation.…”