Super-resolution of Point Set Surfaces using Local Similarities

International audience3D scanners provide a virtual representation of object surfaces at some given precision that depends on many factors such as the object material, the quality of the laser-ray or the resolution of the camera. This precision may even vary over the surface, depending for example on the distance to the scanner which results in uneven and unstructured point sets, with an uncertainty on the coordinates. To enhance the quality of the scanner output, one usually resorts to local surface interpolation between measured points. However, object surfaces often exhibit interesting statistical features such as repetitive geometric textures. Building on this property, we propose a new approach for surface super-resolution that detects repetitive patterns or self-similarities and exploits them to improve the scan resolution by aggregating scattered measures. In contrast with other surface super-resolution methods, our algorithm has two important advantages. First, when handling multiple scans, it does not rely on surface registration. Second, it is able to produce super-resolution from even a single scan. These features are made possible by a new local shape description able to capture differential properties of order above 2. By comparing those descriptors, similarities are detected and used to generate a high-resolution surface. Our results show a clear resolution gain over state-of-the-art interpolation methods

Piecewise smooth reconstruction of normal vector field on digital data

International audienceWe propose a novel method to regularize a normal vector field defined on a digital surface (boundary of a set of voxels). When the digital surface is a digitization of a piecewise smooth manifold, our method localizes sharp features (edges) while regularizing the input normal vector field at the same time. It relies on the optimisation of a variant of the Ambrosio-Tortorelli functional, originally defined for denoising and contour extraction in image processing [AT90]. We reformulate this functional to digital surface processing thanks to discrete calculus operators. Experiments show that the output normal field is very robust to digitization artifacts or noise, and also fairly independent of the sampling resolution. The method allows the user to choose independently the amount of smoothing and the length of the set of discontinuities. Sharp and vanishing features are correctly delineated even on extremely damaged data. Finally, our method can be used to enhance considerably the output of state-of- the-art normal field estimators like Voronoi Covariance Measure [MOG11] or Randomized Hough Transform [BM12]

A spam rejection scheme during SMTP sessions based on layer-3 e-mail classification

This paper proposes a scheme that rejects spam e-mails during their simple mail transfer protocol (SMTP) sessions. This scheme utilizes a layer-3 e-mail classification technique, which allows e-mail classes to be estimated before the end of SMTP sessions at receiving e-mail servers. We analyze the proposed scheme using discrete-time Markov chain analysis under varying e-mail traffic loads and service capacities. This paper also considers the effects of e-mail retransmission and illegal spam relaying by zombie systems on the performance of the proposed scheme. Results from our analysis show that e-mail server loading decreases by using the proposed technique. This allows the reduction in non-spam e-mail queuing delays and loss probability. Our scheme also protects e-mail servers from being overloaded by spam traffic and, if performed collectively over the Internet, it is capable of performing outbound spam control

Author manuscript, published in "CompIMAGE, Buffalo-Niagara: États-Unis (2010)" Curvature Estimation for Discrete Curves Based on Auto-adaptive Masks of Convolution

Abstract. We propose a method that we call auto-adaptive convolution which extends the classical notion of convolution in pictures analysis to function analysis on a discrete set. We define an averaging kernel which takes into account the local geometry of a discrete shape and adapts itself to the curvature. Its defining property is to be local and to follow a normal law on discrete lines of any slope. We used it together with classical differentiation masks to estimate first and second derivatives and give a curvature estimator of discrete functions.

Anisotropic diffusion descriptors

Spectral methods have recently gained popularity in many domains of computer graphics and geometry processing, especially shape processing, computation of shape descriptors, distances, and correspondence. Spectral geometric structures are intrinsic and thus invariant to isometric deformations, are efficiently computed, and can be constructed on shapes in different representations. A notable drawback of these constructions, however, is that they are isotropic, i.e., insensitive to direction. In this paper, we show how to construct direction-sensitive spectral feature descriptors using anisotropic diffusion on meshes and point clouds. The core of our construction are directed local kernels acting similarly to steerable filters, which are learned in a task-specific manner. Remarkably, while being intrinsic, our descriptors allow to disambiguate reflection symmetries. We show the application of anisotropic descriptors for problems of shape correspondence on meshes and point clouds, achieving results significantly better than state-of-the-art methods

One point isometric matching with the heat kernel

Also published as a book chapter: Eurographics Symposium on Geometry Processing 2010 / O. Sorkine and B. Lévy (eds.):1555-1564Maks Ovsjanikov and Quentin Mérigot and Facundo Mémoli and Leonidas Guibashttp://sgp2010.liris.cnrs.fr/cfp.ph