Optimizing Environment Maps for Material Depiction

We present an automated system for optimizing and synthesizing environment maps that enhance the appearance of materials in a scene. We first identify a set of lighting design principles for material depiction. Each principle specifies the distinctive visual features of a material and describes how environment maps can emphasize those features. We express these principles as linear or quadratic image quality metrics, and present a general optimization framework to solve for the environment map that maximizes these metrics. We accelerate metric evaluation using an approach dual to precomputed radiance transfer (PRT). In contrast to standard PRT that integrates light transport over the lighting domain to generate an image, we pre‐integrate light transport over the image domain to optimize for lighting. Finally we present two techniques for transforming existing photographic environment maps to better emphasize materials. We demonstrate the effectiveness of our approach by generating environment maps that enhance the depiction of a variety of materials including glass, metal, plastic, marble and velvet.     

State‐of‐the‐Art Report on Temporal Coherence for Stylized Animations

Non‐photorealistic rendering (NPR) algorithms allow the creation of images in a variety of styles, ranging from line drawing and pen‐and‐ink to oil painting and watercolour. These algorithms provide greater flexibility, control and automation over traditional drawing and painting. Despite significant progress over the past 15 years, the application of NPR to the generation of stylized animations remains an active area of research. The main challenge of computer‐generated stylized animations is to reproduce the look of traditional drawings and paintings while minimizing distracting flickering and sliding artefacts present in hand‐drawn animations. These goals are inherently conflicting and any attempt to address the temporal coherence of stylized animations is a trade‐off. This state‐of‐the‐art report is motivated by the growing number of methods proposed in recent years and the need for a comprehensive analysis of the trade‐offs they propose. We formalize the problem of temporal coherence in terms of goals and compare existing methods accordingly. We propose an analysis for both line and region stylization methods and discuss initial steps towards their perceptual evaluation. The goal of our report is to help uninformed readers to choose the method that best suits their needs, as well as motivate further research to address the limitations of existing methods.     

Revue de Introduction a la Psychologie de l'Enfant (Tomes 1, 2 et 3).

Reviews the book, Introduction a la Psychologie de l'Enfant edited by Michel Hurtig and Jean-Adolphe Rondal (1981). The three volumes of this handbooks covers theories of child psychology as presented by numerous authories in the field. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The 3D Line Motion Matrix and Alignment of Line Reconstructions

We study the problem of aligning two 3D line reconstructions in projective, affine, metric or Euclidean space.We introduce the 6 × 6 3D line motion matrix that acts on Plücker coordinates. We characterize its algebraic properties and its relation to the usual 4 × 4 point motion matrix, and propose various methods for estimating 3D motion from line correspondences, based on cost functions defined in images or 3D space. We assess the quality of the different estimation methods using simulated data and real images.     

Monocular Template-based Reconstruction of Inextensible Surfaces

We present a monocular 3D reconstruction algorithm for inextensible deformable surfaces. It uses point correspondences between a single image of the deformed surface taken by a camera with known intrinsic parameters and a template. The main assumption we make is that the surface shape as seen in the template is known. Since the surface is inextensible, its deformations are isometric to the template. We exploit the distance preservation constraints to recover the 3D surface shape as seen in the image. Though the distance preservation constraints have already been investigated in the literature, we propose a new way to handle them. Spatial smoothness priors are easily incorporated, as well as temporal smoothness priors in the case of reconstruction from a video. The reconstruction can be used for 3D augmented reality purposes thanks to a fast implementation. We report results on synthetic and real data. Some of them are compared to stereo-based 3D reconstructions to demonstrate the efficiency of our method.     

Groupwise geometric and photometric direct image registration

Image registration consists in estimating geometric and photometric transformations that align two images as best as possible. The direct approach consists in minimizing the discrepancy in the intensity or color of the pixels. The inverse compositional algorithm has been recently proposed by Baker et al. for the direct estimation of groupwise geometric transformations. It is efficient in that it performs several computationally expensive calculations at a pre-computation phase. Photometric transformations act on the value of the pixels. They account for effects such as lighting change. Jointly estimating geometric and photometric transformations is thus important for many tasks such as image mosaicing. We propose an algorithm to jointly estimate groupwise geometric and photometric transformations while preserving the efficient pre-computation based design of the original inverse compositional algorithm. It is called the dual inverse compositional algorithm. It uses different approximations than the simultaneous inverse compositional algorithm and handles groupwise geometric and global photometric transformations. Its name stems from the fact that it uses an inverse compositional update rule for both the geometric and the photometric transformations. We demonstrate the proposed algorithm and compare it to previous ones on simulated and real data. This shows clear improvements in computational efficiency and in terms of convergence.     

Procedural generation of villages on arbitrary terrains

Although procedural modeling of cities has attracted a lot of attention for the past decade, populating arbitrary landscapes with non-urban settlements remains an open problem. In this work, we focus on the modeling of small, European villages that took benefit of terrain features to settle in safe, sunny or simply convenient places. We introduce a three step procedural generation method. First, an iterative process based on interest maps is used to progressively generate settlement seeds and the roads that connect them. The fact that a new road attracts settlers while a new house often leads to some extension of the road network is taken into account. Then, an anisotropic conquest method is introduced to segment the land into parcels around settlement seeds. Finally, we introduce open shape grammar to generate 3D geometry that adapts to the local slope. We demonstrate the effectiveness of our method by generating different kinds of village on arbitrary terrains, from a mountain hamlet to a fisherman village, and validate through comparison with real data.     

Approximate schemas,source-consistency and query answering

We use the Edit distance with Moves on words and trees and say that two regular (tree) languages are ε-close if every word (tree) of one language is ε-close to the other. A transducer model is introduced to compare tree languages (schemas) with different alphabets and attributes. Using the statistical embedding of Fischer et al. (Proceedings of 21st IEEE Symposium on Logic in Computer Science, pp. 421–430, 2006), we show that Source-Consistency and Approximate Query Answering are testable on words and trees, i.e. can be approximately decided within ε by only looking at a constant fraction of the input.

A Hybrid approach for integer programming combining genetic algorithms, linear programming and ordinal optimization

Hybrid methods are promising tools in integer programming, as they combine the best features of different methods in a complementary fashion. This paper presents such a framework, integrating the notions of genetic algorithm, linear programming, and ordinal optimization in an effort to shorten computation times for large and/or difficult integer programming problems. Capitalizing on the central idea of ordinal optimization and on the learning capability of genetic algorithms to quickly generate good feasible solutions, and then using linear programming to solve the problem that results from fixing the integer part of the solution, one may be able to obtain solutions that are close to optimal. Indeed ordinal optimization guarantees the quality of the solutions found. Numerical testing on a real-life complex scheduling problem demonstrates the effectiveness and efficiency of this approach.