Michaël Roy

3D part inspection path planning of a laser scanner with control on the uncertainty

This article concerns the measurement process of mechanical parts using laser scanners. From the point of view of industrial applications, the objective is to guarantee the measurement accuracy during the scanning with regard to the geometrical product specifications. The proposed method can be summarized as follows: the first step consists of analyzing the interval of tolerance for the different specifications and to attribute to every geometrical entity a maximal uncertainty of measurement. This uncertainty depends on the angle of incidence between the laser plane and the scanned surface. In the second step, an approach based on the concept of visibility is used from the CAD model of the inspected part to find correct sensor guidance in a metrological point of view. A few position-points from this set are used to define the scanning path. Finally, the measurement can be carried out and the specifications can be controlled after the segmentation of the point clouds. An example illustrates the approach.

MESH COMPARISON USING ATTRIBUTE DEVIATION METRIC

We propose a mesh comparison method using a new attribute deviation metric. The considered meshes contain geometrical and appearance attributes (material color, texture, temperature, etc.). The proposed deviation metric computes local differences between the attributes of two meshes. A mesh comparison assessment can be done easily and quickly using this metric. The techniques proposed are applicable in a number of ways, e.g. 3D matching and registration, and the example described in the paper is the simplification of a surface by iteratively reducing its complexity according to an error metric. The results are presented showing the success of the algorithm through comparisons with other measures and with three different simplification algorithms.

Multiresolution analysis for irregular meshes

The concept of multiresolution analysis applied to irregular meshes has become more and more important. Previous contributions proposed a variety of methods using simplification and/or subdivision algorithms to build a mesh pyramid. In this paper, we propose a multiresolution analysis framework for irregular meshes with attributes. Our framework is based on simplification and subdivision algorithms to build a mesh pyramid. We introduce a surface relaxation operator that allows to build a non-uniform subdivision for a low computational cost. Furthermore, we generalize the relaxation operator to attributes such as color, texture, temperature, etc. The attribute analysis gives more information on the analysed models allowing more complete processing. We show the efficiency of our framework through a number of applications including filtering, denoising and adaptive simplification.

Multiresolution analysis for meshes with appearance attributes

We present a new multiresolution analysis framework for irregular meshes with attributes based on the lifting scheme. We introduce a surface prediction operator to compute the detail coefficients for the geometry and the attributes of the model. Attribute analysis gives appearance information to complete the geometrical analysis of the model. A set of experimental results are given to show the efficiency of our framework. We present two applications to adaptive visualization and denoising.

Generic attribute deviation metric for assessing mesh simplification algorithm quality

This paper describes an efficient method to compare two triangular meshes. Meshes considered here contain geometric features as well as other surface attributes such as material colors, texture, temperature, radiation, etc. Two deviation measurements are presented to assess the differences between two meshes. The first measurement, called geometric deviation, returns geometric differences. The second measurement, called attribute deviation, returns attribute differences regardless of the attribute type. We present an application of this method to the mesh simplification algorithm (MSA) quality assessment according to the appearance attributes. This assessment allows the appreciation of local quality and the computation of global quality statistics of a simplified mesh.

Assessment of mesh simplification algorithm quality

Traditionally, medical geneticists have employed visual inspection (anthroposcopy) to clinically evaluate dysmorphology. In the last 20 years, there has been an increasing trend towards quantitative assessment to render diagnosis of anomalies more objective and reliable. These methods have focused on direct anthropometry, using a combination of classical physical anthropology tools and new instruments tailor-made to describe craniofacial morphometry. These methods are painstaking and require that the patient remain still for extended periods of time. Most recently, semiautomated techniques (e.g., structured light scanning) have been developed to capture the geometry of the face in a matter of seconds. In this paper, we establish that direct anthropometry and structured light scanning yield reliable measurements, with remarkably high levels of inter-rater and intra-rater reliability, as well as validity (contrasting the two methods).

MULTIRESOLUTION ANALYSIS FOR IRREGULAR MESHES WITH APPEARANCE ATTRIBUTES

We present a new multiresolution analysis framework based on the lifting scheme for irregular meshes with attributes. We introduce a surface prediction opera- tor to compute the detail coefficients for the geometry and the attributes of the model. Attribute analysis gives appearance information to complete the geomet- rical analysis of the model.We present an application to adaptive visualization and some experimental results to show the efficiency of our framework.

Denoising 3D models with attributes using soft thresholding

Recent advances in scanning and acquisition technologies allow the construction of complex models from real world scenes. However, the data of those models are generally corrupted by measurement errors. This paper describes an efficient single pass algorithm for denoising irregular meshes of scanned 3D model surfaces. In this algorithm, the frequency content of the model is assessed by a multiresolution analysis that requires only 1-ring neighbourhood without any particular parameterization of the model faces. Denoising is achieved by applying the soft thresholding method to the detail coefficients given by the multiresolution analysis. Our method is suitable for irregular meshes with appearance attributes such as normal vectors and colors. Some results of real world scene models denoised with the proposed algorithm are given to demonstrate its efficiency.

Recent Advances in Multiresolution Analysis of 3D Meshes and their Applications

3D meshes are widely used in computer graphics applications for approximating 3D models. When representing complex shapes in raw data format, meshes consume a large amount of space. Applications calling for compact and fast processing of large 3D meshes have motivated a multitude of algorithms developped to process these datasets efficiently. The concept of multiresolution analysis proposes an efficient and versatile tool for digital geometric processing allowing for numerous applications. In this paper, we survey recent developments in multiresolution methods for 3D triangle meshes. We also show some results of these methods through various applications.

Salient Spin Images: A Descriptor for 3D Object Recognition

In the last decades a wide range of algorithms have been devoted to recognize 3D free-from objects under real conditions such as occlusions, clutters, rotation, scale and translation. Spin image is one of these algorithms known to be robust to rotation, translation, occlusions up to 70% and clutters up to 60%, but still suffer from scaling, resolution changes and it is time consuming. In this paper we present a novel approach based on spin images, called salient spin images (SSI). This method enhances spin images algorithm based on its limits. Particularly, it decreases significantly the complexity of the algorithm using DoG detector, it shows a higher performance due to the relevant localization of salient vertices on the scene, and its robustness to occlusions reaches 80%.