Browsing by Author "Lopez-Moreno, Jorge"

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    Graph-Based Reflectance Segmentation
    (The Eurographics Association, 2021) Garces, Elena; Gutierrez, Diego; Lopez-Moreno, Jorge; Silva, F. and Gutierrez, D. and Rodríguez, J. and Figueiredo, M.
    Most of the unsupervised image segmentation algorithms use just RGB color information in order to establish the similarity criteria between pixels in the image. This leads in many cases to a wrong interpretation of the scene since these criteria do not consider the physical interactions which give raise to of those RGB values (illumination, geometry, albedo) nor our perception of the scene. In this paper, we propose a novel criterion for unsupervised image segmentation which not only relies on color features, but also takes into account an approximation of the materials reflectance. By using a perceptually uniform color space, we apply our criterion to one of the most relevant state of the art segmentation techniques, showing its suitability for segmenting images into small and coherent clusters of constant reflectance. Furthermore, due to the wide adoption of such algorithm, we provide for the first time in the literature an evaluation of this technique under several scenarios and different configurations of its parameters. Finally, in order to enhance both the accuracy of the segmentation and the inner coherence of the clusters, we apply a series of image processing filters to the input image (median, mean-shift, bilateral), analyzing their effects in the segmentation process. Our results can be transferred to any image segmentation algorithm.
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    NEnv: Neural Environment Maps for Global Illumination
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Rodriguez-Pardo, Carlos; Fabre, Javier; Garces, Elena; Lopez-Moreno, Jorge; Ritschel, Tobias; Weidlich, Andrea
    Environment maps are commonly used to represent and compute far-field illumination in virtual scenes. However, they are expensive to evaluate and sample from, limiting their applicability to real-time rendering. Previous methods have focused on compression through spherical-domain approximations, or on learning priors for natural, day-light illumination. These hinder both accuracy and generality, and do not provide the probability information required for importance-sampling Monte Carlo integration. We propose NEnv, a deep-learning fully-differentiable method, capable of compressing and learning to sample from a single environment map. NEnv is composed of two different neural networks: A normalizing flow, able to map samples from uniform distributions to the probability density of the illumination, also providing their corresponding probabilities; and an implicit neural representation which compresses the environment map into an efficient differentiable function. The computation time of environment samples with NEnv is two orders of magnitude less than with traditional methods. NEnv makes no assumptions regarding the content (i.e. natural illumination), thus achieving higher generality than previous learning-based approaches. We share our implementation and a diverse dataset of trained neural environment maps, which can be easily integrated into existing rendering engines.