Agrandissement et réduction | EPFL Graph Search

En géométrie, l’agrandissement et la réduction sont les deux cas de transformations géométriques d'une figure en multipliant ses dimensions par un nombre appelé rapport : ce nombre est supérieur à 1 dans le cas d’un agrandissement, inférieur dans le cas d’une réduction. La figure obtenue est ainsi semblable à l’ancienne, et si les deux apparaissent dans le même plan, elles s’obtiennent chacune par une homothétie sur la figure de l’autre. C’est le cas par exemple d’une configuration de Thalès. Un agrandissement ou une réduction de rapport k multiplie les aires des surfaces homologues par un coefficient k² et les volumes par un coefficient k³. Une carte topographique représente un espace réel selon une réduction dont le rapport est l’échelle indiquée par le document. Voir aussi Théorème de Thalès Bibliographie * * Catégorie:Transformation géométrique Catégorie:Échelle

Does scaling player size skew one’s ability to correctly evaluate object sizes in a virtual environment?

Ronan Boulic, Mathias Guy Delahaye, Bruno Herbelin

This paper presents the results of a study performed in order to evaluate whether a navigation technique, based on scaling the user’s avatar, impacts the user’s ability to correctly assess the size of virtual objects in a virtual environment. This study was realized during the CERN Open Days with data from 177 participants over eighteen years old. It allowed us to observe well-established phenomena, such as the impact of gender on susceptibility to motionsickness, as well as original results associated with scaling factor and avatar embodiment. We observed that the user is more prone to overestimate object sizes from the Virtual Environment when provided with an avatar, while scaling the Inter-Pupillary Distance according to the scale of the user contributes to a reduction in the overestimation of object sizes within the Virtual Environment.

Association for Computing Machinery2020

Resolving Ambiguities in Monocular 3D Reconstruction of Deformable Surfaces

Aydin Varol

In this thesis, we focus on the problem of recovering 3D shapes of deformable surfaces from a single camera. This problem is known to be ill-posed as for a given 2D input image there exist many 3D shapes that give visually identical projections. We present three methods which make headway towards resolving these ambiguities. We believe that our work represents a significant step towards making surface reconstruction methods of practical use. First, we propose a surface reconstruction method that overcomes the limitations of the state-of-the-art template-based and non-rigid structure from motion methods. We neither track points over many frames, nor require a sophisticated deformation model, or depend on a reference image. In our method, we establish correspondences between pairs of frames in which the shape is different and unknown. We then estimate homographies between corresponding local planar patches in both images. These yield approximate 3D reconstructions of points within each patch up to a scale factor. Since we consider overlapping patches, we can enforce them to be consistent over the whole surface. Finally, a local deformation model is used to fit a triangulated mesh to the 3D point cloud, which makes the reconstruction robust to both noise and outliers in the image data. Second, we propose a novel approach to recovering the 3D shape of a deformable surface from a monocular input by taking advantage of shading information in more generic contexts than conventional Shape-from-Shading (SfS) methods. This includes surfaces that may be fully or partially textured and lit by arbitrarily many light sources. To this end, given a lighting model, we learn the relationship between a shading pattern and the corresponding local surface shape. At run time, we first use this knowledge to recover the shape of surface patches and then enforce spatial consistency between the patches to produce a global 3D shape. Instead of treating texture as noise as in many SfS approaches, we exploit it as an additional source of information. We validate our approach quantitatively and qualitatively using both synthetic and real data. Third, we introduce a constrained latent variable model that inherently accounts for geometric constraints such as inextensibility defined on the mesh model. To this end, we learn a non-linear mapping from the latent space to the output space, which corresponds to vertex positions of a mesh model, such that the generated outputs comply with equality and inequality constraints expressed in terms of the problem variables. Since its output is encouraged to satisfy such constraints inherently, using our model removes the need for computationally expensive methods that enforce these constraints at run time. In addition, our approach is completely generic and could be used in many other different contexts as well, such as image classification to impose separation of the classes, and articulated tracking to constrain the space of possible poses.

EPFL2012

Aggressively Truncated Taylor Series Method For Accurate Computation Of Exponentials Of Essentially Nonnegative Matrices

Meiyue Shao

Small relative perturbations to the entries of an essentially nonnegative matrix introduce small relative errors to entries of its exponential. It is thus desirable to compute the exponential with high componentwise relative accuracy. Taylor series approximation coupled with scaling and squaring is used to compute the exponential of an essentially nonnegative matrix. An a priori componentwise relative error bound of truncation is established, from which one can choose the degree of Taylor series expansion and the scale factor so that the exponential is computed with desired componentwise relative accuracy. To reduce the computational cost, the degree of the Taylor series expansion is chosen small, while the scale factor is chosen sufficiently large to achieve the desired accuracy. The rounding errors in the squaring stage are not serious as squaring is forward stable for nonnegative matrices. We also establish a posteriori componentwise error bounds and derive a novel interval algorithm for the matrix exponential of an essentially nonnegative matrix. Rounding error analysis and numerical experiments demonstrate the efficiency and accuracy of the proposed methods.

Siam Publications2014

Resolving Ambiguities in Monocular 3D Reconstruction of Deformable Surfaces

Aydin Varol

EPFL2012