Browsing by Author "Marques, Ricardo"

Now showing 1 - 4 of 4
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    Dynamic Combination of Crowd Steering Policies Based on Context
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Cabrero-Daniel, Beatriz; Marques, Ricardo; Hoyet, Ludovic; Pettré, Julien; Blat, Josep; Chaine, Raphaëlle; Kim, Min H.
    Simulating crowds requires controlling a very large number of trajectories of characters and is usually performed using crowd steering algorithms. The question of choosing the right algorithm with the right parameter values is of crucial importance given the large impact on the quality of results. In this paper, we study the performance of a number of steering policies (i.e., simulation algorithm and its parameters) in a variety of contexts, resorting to an existing quality function able to automatically evaluate simulation results. This analysis allows us to map contexts to the performance of steering policies. Based on this mapping, we demonstrate that distributing the best performing policies among characters improves the resulting simulations. Furthermore, we also propose a solution to dynamically adjust the policies, for each agent independently and while the simulation is running, based on the local context each agent is currently in. We demonstrate significant improvements of simulation results compared to previous work that would optimize parameters once for the whole simulation, or pick an optimized, but unique and static, policy for a given global simulation context.
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    GPU Ray Casting
    (The Eurographics Association, 2021) Marques, Ricardo; Leškovský, Peter; Santos, Luís Paulo; Paloc, Céline; Coelho, António and Cláudio, Ana Paula
    For many applications, such as walk-throughs or terrain visualization, drawing geometric primitives is the most efficient and effective way to represent the data. In contmst, other applications require the visualiza-tion of data that is inherently volumetric. For example, in biomedical imaging, it might be necessary to visualize 3D datasets obtained from GT or J\1RI scanners as a meaningful 2D image, in a process called volume rendering. As a result of the popularity and usefulness of volume data, a broad class of volume rendering techniques has emerged. Ray casting is one of these techniques. It allows for high quality volume rendering, but is a computationally expensive technique which, with current technology, lacks interactivity when visualizing large datasets, if processed on the CPU. The advent of efficient GPUs, available on almost every modern workstations, combined with their high deg ree of programmability opens up a wide field of new applications for the graphics cards. Ray casting is among these applications, exhibiting an intrinsic parallelism, in the form of completely independent light rays, which allows to take advantage of the massively parallel architecture of the GPU. This paper describes the implementation and analysis of a set of shaders which allow interactive volume rendering on the GPU by resorting to my casting techniques.
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    Optimal Sample Weights for Hemispherical Integral Quadratures
    (© 2019 The Eurographics Association and John Wiley & Sons Ltd., 2019) Marques, Ricardo; Bouville, Christian; Bouatouch, Kadi; Chen, Min and Benes, Bedrich
    This paper proposes optimal quadrature rules over the hemisphere for the shading integral. We leverage recent work regarding the theory of quadrature rules over the sphere in order to derive a new theoretical framework for the general case of hemispherical quadrature error analysis. We then apply our framework to the case of the shading integral. We show that our quadrature error theory can be used to derive optimal sample weights (OSW) which account for both the features of the sampling pattern and the bidirectional reflectance distribution function (BRDF). Our method significantly outperforms familiar Quasi Monte Carlo (QMC) and stochastic Monte Carlo techniques. Our results show that the OSW are very effective in compensating for possible irregularities in the sample distribution. This allows, for example, to significantly exceed the regular convergence rate of stochastic Monte Carlo while keeping the exact same sample sets. Another important benefit of our method is that OSW can be applied whatever the sampling points distribution: the sample distribution need not follow a probability density function, which makes our technique much more flexible than QMC or stochastic Monte Carlo solutions. In particular, our theoretical framework allows to easily combine point sets derived from different sampling strategies (e.g. targeted to diffuse and glossy BRDF). In this context, our rendering results show that our approach overcomes MIS (Multiple Importance Sampling) techniques.This paper proposes optimal quadrature rules over the hemisphere for the shading integral. We leverage recent work regarding the theory of quadrature rules over the sphere in order to derive a new theoretical framework for the general case of hemispherical quadrature error analysis. We then apply our framework to the case of the shading integral. We show that our quadrature error theory can be used to derive optimal sample weights (OSW) which account for both the features of the sampling pattern and the material reflectance function (BRDF). Our method significantly outperforms familiar Quasi Monte Carlo (QMC) and stochastic Monte Carlo techniques. Our results show that the OSW are very effective in compensating for possible irregularities in the sample distribution. This allows, for example, to significantly exceed the regular convergence rate of stochastic Monte Carlo while keeping the exact same sample sets.
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    A Perceptually-Validated Metric for Crowd Trajectory Quality Evaluation
    (ACM, 2021) Daniel, Beatriz Cabrero; Marques, Ricardo; Hoyet, Ludovic; Pettré, Julien; Blat, Josep; Narain, Rahul and Neff, Michael and Zordan, Victor
    Simulating crowds requires controlling a very large number of trajectories and is usually performed using crowd motion algorithms for which appropriate parameter values need to be found. The study of the relation between parametric values for simulation techniques and the quality of the resulting trajectories has been studied either through perceptual experiments or by comparison with real crowd trajectories. In this paper, we integrate both strategies. A quality metric, QF, is proposed to abstract from reference data while capturing the most salient features that affect the perception of trajectory realism. QF weights and combines cost functions that are based on several individual, local and global properties of trajectories. These trajectory features are selected from the literature and from interviews with experts. To validate the capacity of QF to capture perceived trajectory quality, we conduct an online experiment that demonstrates the high agreement between the automatic quality score and non-expert users. To further demonstrate the usefulness of QF , we use it in a data-free parameter tuning application able to tune any parametric microscopic crowd simulation model that outputs independent trajectories for characters. The learnt parameters for the tuned crowd motion model maintain the influence of the reference data which was used to weight the terms of QF.