Eurographics Conferences

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EUROGRAPHICS 2024, Limassol: Conference Procceedings online

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Browsing Eurographics Conferences by Subject "3D Graphics and Realism"

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    Automatic Modeling of Planar-Hinged Buildings
    (The Eurographics Association, 2013) Garcia-Dorado, Ignacio; Aliaga, Daniel G.; M.- A. Otaduy and O. Sorkine
    We present a framework to automatically model and reconstruct buildings in a dense urban area. Our method is robust to noise and recovers planar features and sharp edges, producing a water-tight triangulation suitable for texture mapping and interactive rendering. Building and architectural priors, such as the Manhattan world and Atlanta world assumptions, have been used to improve the quality of reconstructions. We extend the framework to include buildings consisting of arbitrary planar faces interconnected by hinges. Given millions of initial 3D points and normals (i.e., via an image-based reconstruction), we estimate the location and properties of the building model hinges and planar segments. Then, starting with a closed Poisson triangulation, we use an energy-based metric to iteratively refine the initial model so as to attempt to recover the planar-hinged model and maintain building details where possible. Our results include automatically reconstructing a variety of buildings spanning a large and dense urban area, comparisons, and analysis of our method. The end product is an automatic method to produce watertight models that are very suitable for 3D city modeling and computer graphics applications.
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    Discovering New Monte Carlo Noise Filters with Genetic Programming
    (The Eurographics Association, 2017) Kán, Peter; Davletaliyev, Maxim; Kaufmann, Hannes; Adrien Peytavie and Carles Bosch
    This paper presents a novel method for the discovery of new analytical filters suitable for filtering of noise in Monte Carlo rendering. Our method utilizes genetic programming to evolve the set of analytical filtering expressions with the goal to minimize image error in training scenes. We show that genetic programming is capable of learning new filtering expressions with quality comparable to state of the art noise filters in Monte Carlo rendering. Additionally, the analytical nature of the resulting expressions enables the run-times one order of magnitude faster than compared state of the art methods. Finally, we present a new analytical filter discovered by our method which is suitable for filtering of Monte Carlo noise in diffuse scenes.