Browsing by Author "Vitsas, Nick"

Now showing 1 - 4 of 4
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    Parallel Transformation of Bounding Volume Hierarchies into Oriented Bounding Box Trees
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Vitsas, Nick; Evangelou, Iordanis; Papaioannou, Georgios; Gkaravelis, Anastasios; Myszkowski, Karol; Niessner, Matthias
    Oriented bounding box (OBB) hierarchies can be used instead of hierarchies based on axis-aligned bounding boxes (AABB), providing tighter fitting to the underlying geometric structures and resulting in improved interference tests, such as ray-geometry intersections. In this paper, we present a method for the fast, parallel transformation of an existing bounding volume hierarchy (BVH), based on AABBs, into a hierarchy based on oriented bounding boxes. To this end, we parallelise a high-quality OBB extraction algorithm from the literature to operate as a standalone OBB estimator and further extend it to efficiently build an OBB hierarchy in a bottom up manner. This agglomerative approach allows for fast parallel execution and the formation of arbitrary, high-quality OBBs in bounding volume hierarchies. The method is fully implemented on the GPU and extensively evaluated with ray intersections.
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    Rayground: An Online Educational Tool for Ray Tracing
    (The Eurographics Association, 2020) Vitsas, Nick; Gkaravelis, Anastasios; Vasilakis, Andreas-Alexandros; Vardis, Konstantinos; Papaioannou, Georgios; Romero, Mario and Sousa Santos, Beatrice
    In this paper, we present Rayground; an online, interactive education tool for richer in-class teaching and gradual self-study, which provides a convenient introduction into practical ray tracing through a standard shader-based programming interface. Setting up a basic ray tracing framework via modern graphics APIs, such as DirectX 12 and Vulkan, results in complex and verbose code that can be intimidating even for very competent students. On the other hand, Rayground aims to demystify ray tracing fundamentals, by providing a well-defined WebGL-based programmable graphics pipeline of configurable distinct ray tracing stages coupled with a simple scene description format. An extensive discussion is further offered describing how both undergraduate and postgraduate computer graphics theoretical lectures and laboratory sessions can be enhanced by our work, to achieve a broad understanding of the underlying concepts. Rayground is open, cross-platform, and available to everyone.
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    Sampling Clear Sky Models using Truncated Gaussian Mixtures
    (The Eurographics Association, 2021) Vitsas, Nick; Vardis, Konstantinos; Papaioannou, Georgios; Bousseau, Adrien and McGuire, Morgan
    Parametric clear sky models are often represented by simple analytic expressions that can efficiently generate plausible, natural radiance maps of the sky, taking into account expensive and hard to simulate atmospheric phenomena. In this work, we show how such models can be complemented by an equally simple, elegant and generic analytic continuous probability density function (PDF) that provides a very good approximation to the radiance-based distribution of the sky. We describe a fitting process that is used to properly parameterise a truncated Gaussian mixture model, which allows for exact, constant-time and minimal-memory sampling and evaluation of this PDF, without rejection sampling, an important property for practical applications in offline and real-time rendering. We present experiments in a standard importance sampling framework that showcase variance reduction approaching that of a more expensive inversion sampling method using Summed Area Tables.
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    Shape Classification of Building Information Models using Neural Networks
    (The Eurographics Association, 2021) Evangelou, Iordanis; Vitsas, Nick; Papaioannou, Georgios; Georgioudakis, Manolis; Chatzisymeon, Apostolos; Biasotti, Silvia and Dyke, Roberto M. and Lai, Yukun and Rosin, Paul L. and Veltkamp, Remco C.
    The Building Information Modelling (BIM) procedure introduces specifications and data exchange formats widely used by the construction industry to describe functional and geometric elements of building structures in the design, planning, cost estimation and construction phases of large civil engineering projects. In this paper we explain how to apply a modern, low-parameter, neural-network-based classification solution to the automatic geometric BIM element labeling, which is becoming an increasingly important task in software solutions for the construction industry. The network is designed so that it extracts features regarding general shape, scale and aspect ratio of each BIM element and be extremely fast during training and prediction. We evaluate our network architecture on a real BIM dataset and showcase accuracy that is difficult to achieve with a generic 3D shape classification network.