1,035 research outputs found
Adaptable Demonstrator Platform for the Simulation of Distributed Agent-Based Automotive Systems
Future autonomous vehicles will no longer have a driver as a fallback solution in case of critical failure scenarios. However, it is costly to add hardware redundancy to achieve a fail-operational behaviour. Here, graceful degradation can be used by repurposing the allocated resources of non-critical applications for safety-critical applications. The degradation problem can be solved as a part of an application mapping problem. As future automotive software will be highly customizable to meet customers\u27 demands, the mapping problem has to be solved for each individual configuration and the architecture has to be adaptable to frequent software changes. Thus, the mapping problem has to be solved at run-time as part of the software platform. In this paper we present an adaptable demonstrator platform consisting of a distributed simulation environment to evaluate such approaches. The platform can be easily configured to evaluate different hardware architectures. We discuss the advantages and limitations of this platform and present an exemplary demonstrator configuration running an agent-based graceful degradation approach
Fast Neural Representations for Direct Volume Rendering
Despite the potential of neural scene representations to effectively compress
3D scalar fields at high reconstruction quality, the computational complexity
of the training and data reconstruction step using scene representation
networks limits their use in practical applications. In this paper, we analyze
whether scene representation networks can be modified to reduce these
limitations and whether such architectures can also be used for temporal
reconstruction tasks. We propose a novel design of scene representation
networks using GPU tensor cores to integrate the reconstruction seamlessly into
on-chip raytracing kernels, and compare the quality and performance of this
network to alternative network- and non-network-based compression schemes. The
results indicate competitive quality of our design at high compression rates,
and significantly faster decoding times and lower memory consumption during
data reconstruction. We investigate how density gradients can be computed using
the network and show an extension where density, gradient and curvature are
predicted jointly. As an alternative to spatial super-resolution approaches for
time-varying fields, we propose a solution that builds upon latent-space
interpolation to enable random access reconstruction at arbitrary granularity.
We summarize our findings in the form of an assessment of the strengths and
limitations of scene representation networks \changed{for compression domain
volume rendering, and outline future research directions
Evaluating embodied conversational agents in multimodal interfaces
Based on cross-disciplinary approaches to Embodied Conversational Agents, evaluation methods for such human-computer interfaces are structured and presented. An introductory systematisation of evaluation topics from a conversational perspective is followed by an explanation of social-psychological phenomena studied in interaction with Embodied Conversational Agents, and how these can be used for evaluation purposes. Major evaluation concepts and appropriate assessment instruments – established and new ones – are presented, including questionnaires, annotations and log-files. An exemplary evaluation and guidelines provide hands-on information on planning and preparing such endeavours
Volumetric Isosurface Rendering with Deep Learning-Based Super-Resolution
Rendering an accurate image of an isosurface in a volumetric field typically
requires large numbers of data samples. Reducing the number of required samples
lies at the core of research in volume rendering. With the advent of deep
learning networks, a number of architectures have been proposed recently to
infer missing samples in multi-dimensional fields, for applications such as
image super-resolution and scan completion. In this paper, we investigate the
use of such architectures for learning the upscaling of a low-resolution
sampling of an isosurface to a higher resolution, with high fidelity
reconstruction of spatial detail and shading. We introduce a fully
convolutional neural network, to learn a latent representation generating a
smooth, edge-aware normal field and ambient occlusions from a low-resolution
normal and depth field. By adding a frame-to-frame motion loss into the
learning stage, the upscaling can consider temporal variations and achieves
improved frame-to-frame coherence. We demonstrate the quality of the network
for isosurfaces which were never seen during training, and discuss remote and
in-situ visualization as well as focus+context visualization as potential
application
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