33,570 research outputs found
Three architectures for volume rendering
Volume rendering is a key technique in scientific visualization that lends itself to significant exploitable parallelism. The high computational demands of real-time volume rendering and continued technological advances in the area of VLSI give impetus to the development of special-purpose volume rendering architectures. This paper presents and characterizes three recently developed volume rendering engines which are based on the ray-casting method. A taxonomy of the algorithmic variants of ray-casting and details of each ray-casting architecture are discussed. The paper then compares the machine features and provides an outlook on future developments in the area of volume rendering hardware
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Architectures for Real-Time Volume Rendering
Over the last decade, volume rendering has become an invaluable visualization technique for a wide variety of applications. This paper reviews three special-purpose architectures for interactive volume rendering: texture mapping, VIRIM, and VolumePro. Commercial implementations of these architectures are available or underway. The discussion of each architecture will focus on the algorithm, system architecture, memory system, and volume rendering performance.Engineering and Applied Science
A Distributed GPU-based Framework for real-time 3D Volume Rendering of Large Astronomical Data Cubes
We present a framework to interactively volume-render three-dimensional data
cubes using distributed ray-casting and volume bricking over a cluster of
workstations powered by one or more graphics processing units (GPUs) and a
multi-core CPU. The main design target for this framework is to provide an
in-core visualization solution able to provide three-dimensional interactive
views of terabyte-sized data cubes. We tested the presented framework using a
computing cluster comprising 64 nodes with a total of 128 GPUs. The framework
proved to be scalable to render a 204 GB data cube with an average of 30 frames
per second. Our performance analyses also compare between using NVIDIA Tesla
1060 and 2050 GPU architectures and the effect of increasing the visualization
output resolution on the rendering performance. Although our initial focus, and
the examples presented in this work, is volume rendering of spectral data cubes
from radio astronomy, we contend that our approach has applicability to other
disciplines where close to real-time volume rendering of terabyte-order 3D data
sets is a requirement.Comment: 13 Pages, 7 figures, has been accepted for publication in
Publications of the Astronomical Society of Australi
PVD-AL: Progressive Volume Distillation with Active Learning for Efficient Conversion Between Different NeRF Architectures
Neural Radiance Fields (NeRF) have been widely adopted as practical and
versatile representations for 3D scenes, facilitating various downstream tasks.
However, different architectures, including plain Multi-Layer Perceptron (MLP),
Tensors, low-rank Tensors, Hashtables, and their compositions, have their
trade-offs. For instance, Hashtables-based representations allow for faster
rendering but lack clear geometric meaning, making spatial-relation-aware
editing challenging. To address this limitation and maximize the potential of
each architecture, we propose Progressive Volume Distillation with Active
Learning (PVD-AL), a systematic distillation method that enables any-to-any
conversions between different architectures. PVD-AL decomposes each structure
into two parts and progressively performs distillation from shallower to deeper
volume representation, leveraging effective information retrieved from the
rendering process. Additionally, a Three-Levels of active learning technique
provides continuous feedback during the distillation process, resulting in
high-performance results. Empirical evidence is presented to validate our
method on multiple benchmark datasets. For example, PVD-AL can distill an
MLP-based model from a Hashtables-based model at a 10~20X faster speed and
0.8dB~2dB higher PSNR than training the NeRF model from scratch. Moreover,
PVD-AL permits the fusion of diverse features among distinct structures,
enabling models with multiple editing properties and providing a more efficient
model to meet real-time requirements. Project website:http://sk-fun.fun/PVD-AL.Comment: Project website: http://sk-fun.fun/PVD-AL. arXiv admin note:
substantial text overlap with arXiv:2211.1597
GPU Accelerated Particle Visualization with Splotch
Splotch is a rendering algorithm for exploration and visual discovery in
particle-based datasets coming from astronomical observations or numerical
simulations. The strengths of the approach are production of high quality
imagery and support for very large-scale datasets through an effective mix of
the OpenMP and MPI parallel programming paradigms. This article reports our
experiences in re-designing Splotch for exploiting emerging HPC architectures
nowadays increasingly populated with GPUs. A performance model is introduced
for data transfers, computations and memory access, to guide our re-factoring
of Splotch. A number of parallelization issues are discussed, in particular
relating to race conditions and workload balancing, towards achieving optimal
performances. Our implementation was accomplished by using the CUDA programming
paradigm. Our strategy is founded on novel schemes achieving optimized data
organisation and classification of particles. We deploy a reference simulation
to present performance results on acceleration gains and scalability. We
finally outline our vision for future work developments including possibilities
for further optimisations and exploitation of emerging technologies.Comment: 25 pages, 9 figures. Astronomy and Computing (2014
Structure from motion systems for architectural heritage. A survey of the internal loggia courtyard of Palazzo dei Capitani, Ascoli Piceno, Italy
We present the results of a point-cloud-based survey deriving from the use of image-based techniques, in particular with multi-image monoscopic digital photogrammetry systems and software, the so-called “structure-from-motion” technique. The aim is to evaluate the advantages and limitations of such procedures in architectural surveying, particularly in conditions that are “at the limit”. A particular case study was chosen: the courtyard of Palazzo dei Capitani del Popolo in Ascoli Piceno, Italy, which can be considered the ideal example due to its notable vertical, rather than horizontal, layout. In this context, by comparing and evaluating the different results, we present experimentation regarding this single case study with the aim of identifying the best workflow to realise a complex, articulated set of representations—using 3D modelling and 2D processing—necessary to correctly document the particular characteristics of such an architectural object
Analyzing and Modeling the Performance of the HemeLB Lattice-Boltzmann Simulation Environment
We investigate the performance of the HemeLB lattice-Boltzmann simulator for
cerebrovascular blood flow, aimed at providing timely and clinically relevant
assistance to neurosurgeons. HemeLB is optimised for sparse geometries,
supports interactive use, and scales well to 32,768 cores for problems with ~81
million lattice sites. We obtain a maximum performance of 29.5 billion site
updates per second, with only an 11% slowdown for highly sparse problems (5%
fluid fraction). We present steering and visualisation performance measurements
and provide a model which allows users to predict the performance, thereby
determining how to run simulations with maximum accuracy within time
constraints.Comment: Accepted by the Journal of Computational Science. 33 pages, 16
figures, 7 table
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