Jean-Luc Peyrot

Feature-preserving Direct Blue Noise Sampling for Surface Meshes

We present a new direct Poisson disk sampling for surface meshes. Our objective is to sample triangular meshes, while satisfying good blue noise properties, but also preserving features. Our method combines a feature detection technique based on vertex curvature, and geodesic-based dart throwing. Our method is fast, automatic, and experimental results prove that our method is well-suited to CAD models, since it handles sharp features and high genus meshes, while having good blue noise properties.

Direct blue noise resampling of meshes of arbitrary topology

We propose in this paper a novel sampling method and an improvement of a spectral analysis tool that both handle complex shapes and sharp features. Starting from an arbitrary triangular mesh, our algorithm generates a new sampling pattern that exhibits blue noise properties. The fidelity to the original surface being essential, our algorithm preserves sharp features. Our sampling is based on a discrete dart throwing applied directly on the surface to get good blue noise sampling patterns. It is also driven by a feature detection tool to avoid geometric aliasing. Experimental results prove that our sampling scheme is faster than techniques based on brute-force dart throwing, and produces sampling patterns with blue noise properties even for complex surfaces of arbitrary topology. In parallel, we also propose an improvement of a tool initially developed for the spectral analysis of non-uniform sampling patterns, that may generate biased results with complex shapes. The proposed improvement overcomes this problem.

Stereo reconstruction of semiregular meshes, and multiresolution analysis for automatic detection of dents on surfaces

Our objective is to include in stereoscopic 3D acquisition systems new technologies to automatically detect deformations on aircraft fuselages. We propose in this paper a semiregular mesh reconstruction dedicated to stereoscopic scanners, combined to a multiresolution analysis tool that detects dents on smooth surfaces. The proposed technique for reconstruction is based on a coarse-to-fine approach, and creates semiregular meshes directly from the stereoscopic images. The output of our scanner is thus a structured mesh, by the way well suited for many applications unlike most of scanners that generate only point clouds. Local distances are then calculated between this semiregular mesh and a smooth version of it, in order to automatically detect dents on the scanned surface. The smooth version is obtained via a technique based on multiresolution analysis. Experimental results show the reliability of our contributions on scanned aircraft fuselages.

From stereoscopic images to semi-regular meshes

The pipeline to get the semi-regular mesh of a specific physical object is long and fastidious: physical acquisition (creating a dense point cloud), cleaning/meshing (creating an irregular triangle mesh), and semi-regular remeshing. Moreover, these three stages are generally independent, and processed successively by different tools. To overcome this issue, we propose in this paper a new framework to design semi-regular meshes directly from stereoscopic images. Our semi-regular reconstruction technique first creates a base mesh by using a feature-preserving sampling on the stereoscopic images. Afterwards, this base mesh is passed to a coarse-to-fine meshing process to get the semi-regular mesh of the original surface. Experimental results prove the reliability and the accuracy of our approach in terms of shape fidelity, compactness, but also runtime, since many steps have been parallelized on the GPU. Graphical abstractDisplay Omitted HighlightsDirect semi-regular reconstruction from stereo to simplify the classical pipeline.Based on a hybrid Poisson-disk sampling, and a coarse-to-fine refinement process.Restrict the use of 3D information, and work on the stereo images.Efforts are made to reduce as much as possible the runtime using GPU implementations.Runtimes are faster than prior semi-regular reconstruction method.

Blue noise Sampling of surfaces from stereoscopic images.

We propose an original technique to sample surfaces generated by stereoscopic acquisition systems. Our motivation is to simplify the long and fastidious sampling pipeline, for such acquisition systems. The idea is to make the sampling of the surfaces directly on the pair of stereoscopic images, instead of doing it on the meshes created by triangulation of the point clouds given by the acquisition system. More precisely, we present a feature-preserving sampling, done directly in the stereoscopic image domain, while computing the inter-sample distances in the 3D space, in order to reduce the distortion due the embedding in R3. We focus on Poisson-disk sampling, because of its nice blue noise properties. Experimental results show that our method is a good trade-off between the direct sampling methods that are time-consuming, and the methods based on parameterization that alter the final sampling properties.

Aliasing-free simplification of surface meshes

We propose in this paper a robust simplification technique, which preserves geometric features such as sharp edges or corners from original surfaces. To achieve this goal, our simplification process relies on a detection tool that enables to preserve the sharp features during the three subsequent steps: a Poisson disk sampling that intelligently reduces the number of vertices of the initial mesh; the meshing of the samples that aligns the edges along the feature lines; and a constrained relaxation step that improves the shape of the triangles of our final simplified mesh. Experimental results show that our method always produces valid meshes without aliasing artifacts, and without giving up the shape fidelity and quality of the mesh elements.