93 research outputs found
Web based hybrid volumetric visualisation of urban GIS data: Integration of 4D Temperature and Wind Fields with LoD-2 CityGML models
City models visualisation, buildings, structures and volumetric information, is an important task in Computer Graphics and Urban Planning -- The different formats and data sources involved in the visualisation make the development of applications a big challenge -- We present a homogeneous web visualisation framework using X3DOM and MEDX3DOM for the visualisation of these urban objects -- We present an integration of different declarative data sources, enabling the utilization of advanced visualisation algorithms to render the models -- It has been tested with a city model composed of buildings from the Madrid University Campus, some volumetric datasets coming from Air Quality Models and 2D layers wind datasets -- Results show that the visualisation of all the urban models can be performed in real time on the Web -- An HTML5 web interface is presented to the users, enabling real time modifications of visualisation parameter
Dilated FCN for Multi-Agent 2D/3D Medical Image Registration
2D/3D image registration to align a 3D volume and 2D X-ray images is a
challenging problem due to its ill-posed nature and various artifacts presented
in 2D X-ray images. In this paper, we propose a multi-agent system with an auto
attention mechanism for robust and efficient 2D/3D image registration.
Specifically, an individual agent is trained with dilated Fully Convolutional
Network (FCN) to perform registration in a Markov Decision Process (MDP) by
observing a local region, and the final action is then taken based on the
proposals from multiple agents and weighted by their corresponding confidence
levels. The contributions of this paper are threefold. First, we formulate
2D/3D registration as a MDP with observations, actions, and rewards properly
defined with respect to X-ray imaging systems. Second, to handle various
artifacts in 2D X-ray images, multiple local agents are employed efficiently
via FCN-based structures, and an auto attention mechanism is proposed to favor
the proposals from regions with more reliable visual cues. Third, a dilated
FCN-based training mechanism is proposed to significantly reduce the Degree of
Freedom in the simulation of registration environment, and drastically improve
training efficiency by an order of magnitude compared to standard CNN-based
training method. We demonstrate that the proposed method achieves high
robustness on both spine cone beam Computed Tomography data with a low
signal-to-noise ratio and data from minimally invasive spine surgery where
severe image artifacts and occlusions are presented due to metal screws and
guide wires, outperforming other state-of-the-art methods (single agent-based
and optimization-based) by a large margin.Comment: AAAI 201
Interactive Camera Network Design using a Virtual Reality Interface
Traditional literature on camera network design focuses on constructing
automated algorithms. These require problem specific input from experts in
order to produce their output. The nature of the required input is highly
unintuitive leading to an unpractical workflow for human operators. In this
work we focus on developing a virtual reality user interface allowing human
operators to manually design camera networks in an intuitive manner. From real
world practical examples we conclude that the camera networks designed using
this interface are highly competitive with, or superior to those generated by
automated algorithms, but the associated workflow is much more intuitive and
simple. The competitiveness of the human-generated camera networks is
remarkable because the structure of the optimization problem is a well known
combinatorial NP-hard problem. These results indicate that human operators can
be used in challenging geometrical combinatorial optimization problems given an
intuitive visualization of the problem.Comment: 11 pages, 8 figure
A Framework for Megascale Agent Based Model Simulations on Graphics Processing Units
Agent-based modeling is a technique for modeling dynamic systems from the bottom up. Individual elements of the system are represented computationally as agents. The system-level behaviors emerge from the micro-level interactions of the agents. Contemporary state-of-the-art agent-based modeling toolkits are essentially discrete-event simulators designed to execute serially on the Central Processing Unit (CPU). They simulate Agent-Based Models (ABMs) by executing agent actions one at a time. In addition to imposing an un-natural execution order, these toolkits have limited scalability. In this article, we investigate data-parallel computer architectures such as Graphics Processing Units (GPUs) to simulate large scale ABMs. We have developed a series of efficient, data parallel algorithms for handling environment updates, various agent interactions, agent death and replication, and gathering statistics. We present three fundamental innovations that provide unprecedented scalability. The first is a novel stochastic memory allocator which enables parallel agent replication in O(1) average time. The second is a technique for resolving precedence constraints for agent actions in parallel. The third is a method that uses specialized graphics hardware, to gather and process statistical measures. These techniques have been implemented on a modern day GPU resulting in a substantial performance increase. We believe that our system is the first ever completely GPU based agent simulation framework. Although GPUs are the focus of our current implementations, our techniques can easily be adapted to other data-parallel architectures. We have benchmarked our framework against contemporary toolkits using two popular ABMs, namely, SugarScape and StupidModel.GPGPU, Agent Based Modeling, Data Parallel Algorithms, Stochastic Simulations
Immersive Neural Graphics Primitives
Neural radiance field (NeRF), in particular its extension by instant neural
graphics primitives, is a novel rendering method for view synthesis that uses
real-world images to build photo-realistic immersive virtual scenes. Despite
its potential, research on the combination of NeRF and virtual reality (VR)
remains sparse. Currently, there is no integration into typical VR systems
available, and the performance and suitability of NeRF implementations for VR
have not been evaluated, for instance, for different scene complexities or
screen resolutions. In this paper, we present and evaluate a NeRF-based
framework that is capable of rendering scenes in immersive VR allowing users to
freely move their heads to explore complex real-world scenes. We evaluate our
framework by benchmarking three different NeRF scenes concerning their
rendering performance at different scene complexities and resolutions.
Utilizing super-resolution, our approach can yield a frame rate of 30 frames
per second with a resolution of 1280x720 pixels per eye. We discuss potential
applications of our framework and provide an open source implementation online.Comment: Submitted to IEEE VR, currently under revie
Interactive ray shading of FRep objects
In this paper we present a method for interactive rendering general procedurally defined functionally represented (FRep) objects using the acceleration with graphics hardware, namely Graphics Processing Units (GPU). We obtain interactive rates by using GPU acceleration for all computations in rendering algorithm, such as ray-surface intersection, function evaluation and normal computations. We compute primary rays as well as secondary rays for shadows, reflection and refraction for obtaining high quality of the output visualization and further extension to ray-tracing of FRep objects. The algorithm is well-suited for modern GPUs and provides acceptable interactive rates with good quality of the results. A wide range of objects can be rendered including traditional skeletal implicit surfaces, constructive solids, and purely procedural objects such as 3D fractals
Free-Surface Lattice-Boltzmann Simulation on Many-Core Architectures
AbstractCurrent advances in many-core technologies demand simulation algorithms suited for the corresponding architectures while with regard to the respective increase of computational power, real-time and interactive simulations become possible and desirable. We present an OpenCL implementation of a Lattice-Boltzmann-based free-surface solver for GPU architectures. The massively parallel execution especially requires special techniques to keep the interface region consistent, which is here addressed by a novel multipass method. We further compare different memory layouts according to their performance for both a basic driven cavity implementation and the free-surface method, pointing out the capabilities of our implementation in real-time and interactive scenarios, and shortly present visualizations of the flow, obtained in real-time
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