7,446 research outputs found
Adaptive transfer functions: improved multiresolution visualization of medical models
The final publication is available at Springer via http://dx.doi.org/10.1007/s00371-016-1253-9Medical datasets are continuously increasing in size. Although larger models may be available for certain research purposes, in the common clinical practice the models are usually of up to 512x512x2000 voxels. These resolutions exceed the capabilities of conventional GPUs, the ones usually found in the medical doctors’ desktop PCs. Commercial solutions typically reduce the data by downsampling the dataset iteratively until it fits the available target specifications. The data loss reduces the visualization quality and this is not commonly compensated with other actions that might alleviate its effects. In this paper, we propose adaptive transfer functions, an algorithm that improves the transfer function in downsampled multiresolution models so that the quality of renderings is highly improved. The technique is simple and lightweight, and it is suitable, not only to visualize huge models that would not fit in a GPU, but also to render not-so-large models in mobile GPUs, which are less capable than their desktop counterparts. Moreover, it can also be used to accelerate rendering frame rates using lower levels of the multiresolution hierarchy while still maintaining high-quality results in a focus and context approach. We also show an evaluation of these results based on perceptual metrics.Peer ReviewedPostprint (author's final draft
Mobile graphics: SIGGRAPH Asia 2017 course
Peer ReviewedPostprint (published version
Volumetric Medical Images Visualization on Mobile Devices
Volumetric medical images visualization is an important tool in the diagnosis
and treatment of diseases. Through history, one of the most dificult
tasks for Medicine Specialists has been the accurate location of broken bones
and of the damaged tissues during Chemotherapy treatment, among other
applications; like techniques used in Neurological Studies. Thus these situations
enhance the need of visualization in Medicine. New technologies,
the improvement and development of new hardware as well as software and
the updating of old ones for graphic applications have resulted in specialized
systems for medical visualization. However the use of these techniques
in mobile devices has been poor due to its low performance. In our work,
we propose a client-server scheme, where the model is compressed in the
server side and is reconstructed in a nal thin-client device. The technique
restricts the natural density values to achieve good bone visualization in
medical models, transforming the rest of the data to zero. Our proposal
uses a tridimensional Haar Wavelet Function locally applied inside units
blocks of 16x16x16, similar to the Wavelet Based 3D Compression Scheme
for Interactive Visualization of Very Large Volume Data approach. We also
implement a quantization algorithm which handles error coeficients according
to the frequency distributions of these coe cients. Finally, we made
an evaluation of the volume visualization; on current mobile devices .We
present the speci cations for the implementation of our technique in the
Nokia n900 Mobile Phone
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
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