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Item EUROGRAPHICS 2016: Tutorials Frontmatter(Eurographics Association, 2016) Sousa, A. Augusto; Bouatouch, Kadi;Item ATIP: A Tool for 3D Navigation inside a Single Image with Automatic Camera Calibration(The Eurographics Association, 2006) Boulanger, Kevin; Bouatouch, Kadi; Pattanaik, Sumanta; Louise M. Lever and Mary McDerbyAutomatic Tour Into the Picture (ATIP) is an extension of the Tour Into the Picture method [HAA97] that allows an approximative but visually convincing 3D walk-through inside a single image by rendering a box textured using the input image data. The original algorithm requires a long and tedious user interaction to determine the box dimensions and the perspective parameters, and imposes several constraints on the input image orientation. The goal of this paper is to present a framework providing fully automatic and fast camera calibration for any view orientation without using a calibration target. Our method reduces the user interaction, hence only a couple of seconds are required between the input image loading and the final walk-through.Item Spatial Directional Radiance Caching(The Eurographics Association and Blackwell Publishing Ltd, 2009) Gassenbauer, Vaclav; Krivanek, Jaroslav; Bouatouch, KadiWe present a new approach for accelerated global illumination computation in scenes with glossy surfaces. Our algorithm combines sparse illumination computation used in the radiance caching algorithm with BRDF importance sampling. To make this approach feasible, we extend the idea of lazy illumination evaluation, used in the caching approaches, from the spatial to the directional domain. Using importance sampling allows us to apply caching not only on low-gloss but also on shiny materials with high-frequency BRDFs, for which the radiance caching algorithm breaks down.Item Improving Performance and Accuracy of Local PCA(The Eurographics Association and Blackwell Publishing Ltd., 2011) Gassenbauer, Václav; Krivánek, Jaroslav; Bouatouch, Kadi; Bouville, Christian; Ribardière, Mickaël; Bing-Yu Chen, Jan Kautz, Tong-Yee Lee, and Ming C. LinLocal Principal Component Analysis (LPCA) is one of the popular techniques for dimensionality reduction and data compression of large data sets encountered in computer graphics. The LPCA algorithm is a variant of kmeans clustering where the repetitive classification of high dimensional data points to their nearest cluster leads to long execution times. The focus of this paper is on improving the efficiency and accuracy of LPCA. We propose a novel SortCluster LPCA algorithm that significantly reduces the cost of the point-cluster classification stage, achieving a speed-up of up to 20. To improve the approximation accuracy, we investigate different initialization schemes for LPCA and find that the k-means++ algorithm [AV07] yields best results, however at a high computation cost. We show that similar ideas that lead to the efficiency of our SortCluster LPCA algorithm can be used to accelerate k-means++. The resulting initialization algorithm is faster than purely random seeding while producing substantially more accurate data approximation.Item Automatic Aesthetics-based Lighting Design with Global Illumination(The Eurographics Association, 2014) Léon, Vincent; Gruson, Adrien; Cozot, Rémi; Bouatouch, Kadi; John Keyser and Young J. Kim and Peter WonkaIn computer graphics, lighting plays an important role in the appearance of a scene. A change in the configuration of light sources can lead to different aesthetics in the final rendered image. Lighting design becomes increasingly complex when using sophisticated global illumination techniques. In this paper, we present a new approach to automatically design the lighting configuration according to the aesthetic goal specified by the user as a set of target parameters. Target parameters are used to set up an objective function which is minimized using an optimization method. The results show that our method can be used to automatically design a lighting configuration that will give to the final image a classic photographic look.Item Bayesian and Quasi Monte Carlo Spherical Integration for Illumination Integrals(The Eurographics Association, 2014) Marques, Ricardo; Bouville, Christian; Bouatouch, Kadi; Nicolas Holzschuch and Karol MyszkowskiThe spherical sampling of the incident radiance function entails a high computational cost. Therefore the illumination integral must be evaluated using a limited set of samples. Such a restriction raises the question of how to obtain the most accurate approximation possible with such a limited set of samples. We need to ensure that sampling produces the highest amount of information possible by carefully placing the limited set of samples. Furthermore we want our integral evaluation to take into account not only the information produced by the sampling but also possible information available prior to sampling. In this tutorial we focus on the case of hemispherical sampling for spherical Monte Carlo (MC) integration. We will show that existing techniques can be improved by making a detailed analysis of the theory of MC spherical integration. We will then use this theory to identify and improve the weak points of current approaches, based on very recent advances in the fields of integration and spherical Quasi-Monte Carlo integration.Item Example‐Based Colour Transfer for 3D Point Clouds(© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Goudé, Ific; Cozot, Rémi; Le Meur, Olivier; Bouatouch, Kadi; Benes, Bedrich and Hauser, HelwigExample‐based colour transfer between images, which has raised a lot of interest in the past decades, consists of transferring the colour of an image to another one. Many methods based on colour distributions have been proposed, and more recently, the efficiency of neural networks has been demonstrated again for colour transfer problems. In this paper, we propose a new pipeline with methods adapted from the image domain to automatically transfer the colour from a target point cloud to an input point cloud. These colour transfer methods are based on colour distributions and account for the geometry of the point clouds to produce a coherent result. The proposed methods rely on simple statistical analysis, are effective, and succeed in transferring the colour style from one point cloud to another. The qualitative results of the colour transfers are evaluated and compared with existing methods.Item Two-Level Adaptive Sampling for Illumination Integrals using Bayesian Monte Carlo(The Eurographics Association, 2016) Marques, Ricardo; Bouville, Christian; Santos, Luis P.; Bouatouch, Kadi; T. Bashford-Rogers and L. P. SantosBayesian Monte Carlo (BMC) is a promising integration technique which considerably broadens the theoretical tools that can be used to maximize and exploit the information produced by sampling, while keeping the fundamental property of data dimension independence of classical Monte Carlo (CMC). Moreover, BMC uses information that is ignored in the CMC method, such as the position of the samples and prior stochastic information about the integrand, which often leads to better integral estimates. Nevertheless, the use of BMC in computer graphics is still in an incipient phase and its application to more evolved and widely used rendering algorithms remains cumbersome. In this article we propose to apply BMC to a two-level adaptive sampling scheme for illumination integrals. We propose an efficient solution for the second level quadrature computation and show that the proposed method outperforms adaptive quasi-Monte Carlo in terms of image error and high frequency noise.Item Context-aware Clustering and Assessment of Photo Collections(Association for Computing Machinery, Inc (ACM), 2017) Kuzovkin, Dmitry; Pouli, Tania; Cozot, Rémi; Meur, Olivier Le; Kervec, Jonathan; Bouatouch, Kadi; Holger Winnemoeller and Lyn BartramTo ensure that all important moments of an event are represented and that challenging scenes are correctly captured, both amateur and professional photographers often opt for taking large quantities of photographs. As such, they are faced with the tedious task of organizing large collections and selecting the best images among similar variants. Automatic methods assisting with this task are based on independent assessment approaches, evaluating each image apart from other images in the collection. However, the overall quality of photo collections can largely vary due to user skills and other factors. In this work, we explore the possibility of contextaware image quality assessment, where the photo context is defined using a clustering approach, and statistics of both the extracted context and the entire photo collection are used to guide identification of low-quality photos. We demonstrate that our method is able to exibly adapt to the nature of processed albums and to facilitate the task of image selection in diverse scenarios.Item Making Radiance and Irradiance Caching Practical: Adaptive Caching and Neighbor Clamping(The Eurographics Association, 2006) Krivánek, Jaroslav; Bouatouch, Kadi; Pattanaik, Sumanta; Zára, Jirí; Tomas Akenine-Moeller and Wolfgang HeidrichRadiance and irradiance caching are efficient global illumination algorithms based on interpolating indirect illumination from a sparse set of cached values. In this paper we propose an adaptive algorithm for guiding spatial density of the cached values in radiance and irradiance caching. The density is adapted to the rate of change of indirect illumination in order to avoid visible interpolation artifacts and produce smooth interpolated illumination. In addition, we discuss some practical problems arising in the implementation of radiance and irradiance caching, and propose techniques for solving those problems. Namely, the neighbor clamping heuristic is proposed as a robust means for detecting small sources of indirect illumination and for dealing with problems caused by ray leaking through small gaps between adjacent polygons.