From 3D point clouds to feature preserving meshes

Nader Salman 1
1 GEOMETRICA - Geometric computing
CRISAM - Inria Sophia Antipolis - Méditerranée , Inria Saclay - Ile de France
Abstract : Most of the current surface reconstruction algorithms target high quality data and can produce some intractable results when used with point clouds acquired through profitable 3D acquisition methods. Our first contribution is a surface reconstruction algorithm from stereo vision data that copes with the data's fuzziness using information from both the acquired 3D point cloud and the calibrated images. After pre-processing the point cloud, the algorithm builds, using the calibrated images, a 3D triangular soup consistent with the surface of the scene through a combination of visibility and photo-consistency constraints. A mesh is then computed from the triangle soup using a combination of restricted Delaunay triangulation and Delaunay refinement methods. Our second contribution is an algorithm that builds, given a 3D point cloud sampled on a surface, an approximating surface mesh with an accurate representation of surface sharp edges, providing an enhanced trade-off between accuracy and mesh complexity. We first extract from the point cloud an approximation of the sharp edges of the underlying surface. Then, a feature preserving variant of a Delaunay refinement process generates a mesh combining a faithful representation of the extracted sharp edges with an implicit surface obtained from the point cloud. The method is shown to be flexible, robust to noise and tunable to adapt to the scale of the targeted mesh and to a user defined sizing field. We demonstrate the effectiveness of both contributions on a variety of scenes and models acquired with different hardware and show results that compare favourably, in terms of accuracy, with the current state of the art.
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Nader Salman. From 3D point clouds to feature preserving meshes. Modeling and Simulation. Université Nice Sophia Antipolis, 2010. English. ⟨tel-00536984v2⟩

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