25 research outputs found
Immersive ExaBrick: Visualizing Large AMR Data in the CAVE
Rendering large adaptive mesh refinement (AMR) data in real-time in virtual reality (VR) environments is a complex challenge that demands sophisticated techniques and tools. The proposed solution harnesses the ExaBrick framework and integrates it as a plugin in COVISE, a robust visualization system equipped with the VR-centric OpenCOVER render module. This setup enables direct navigation and interaction within the rendered volume in a VR environment. The user interface incorporates rendering options and functions, ensuring a smooth and interactive experience. We show that high-quality volume rendering of AMR data in VR environments at interactive rates is possible using GPUs
Interactive High Performance Volume Rendering
This thesis is about Direct Volume Rendering on high performance computing systems. As direct rendering methods do not create a lower-dimensional geometric representation, the whole scientific dataset must be kept in memory. Thus, this family of algorithms has a tremendous resource demand. Direct Volume Rendering algorithms in general are well suited to be implemented for dedicated graphics
hardware. Nevertheless, high performance computing systems often do not provide resources for hardware accelerated rendering, so that the visualization algorithm must be implemented for the available general-purpose hardware.
Ever growing datasets that imply copying large amounts of data from the compute system to the workstation of the scientist, and the need to review intermediate simulation results, make porting Direct Volume Rendering to high performance computing systems highly relevant. The contribution of this thesis is twofold.
As part of the first contribution, after devising a software architecture for general implementations of Direct Volume Rendering on highly parallel platforms, parallelization issues and implementation details for various modern architectures are discussed. The contribution results in a highly parallel implementation that tackles several platforms.
The second contribution is concerned with the display phase of the “Distributed Volume Rendering Pipeline”. Rendering on a high performance computing system typically implies displaying the rendered result at a remote location. This thesis presents a remote rendering technique that is capable of hiding latency and can thus be used in an interactive environment
KiloNeuS: A Versatile Neural Implicit Surface Representation for Real-Time Rendering
NeRF-based techniques fit wide and deep multi-layer perceptrons (MLPs) to a
continuous radiance field that can be rendered from any unseen viewpoint.
However, the lack of surface and normals definition and high rendering times
limit their usage in typical computer graphics applications. Such limitations
have recently been overcome separately, but solving them together remains an
open problem. We present KiloNeuS, a neural representation reconstructing an
implicit surface represented as a signed distance function (SDF) from
multi-view images and enabling real-time rendering by partitioning the space
into thousands of tiny MLPs fast to inference. As we learn the implicit surface
locally using independent models, resulting in a globally coherent geometry is
non-trivial and needs to be addressed during training. We evaluate rendering
performance on a GPU-accelerated ray-caster with in-shader neural network
inference, resulting in an average of 46 FPS at high resolution, proving a
satisfying tradeoff between storage costs and rendering quality. In fact, our
evaluation for rendering quality and surface recovery shows that KiloNeuS
outperforms its single-MLP counterpart. Finally, to exhibit the versatility of
KiloNeuS, we integrate it into an interactive path-tracer taking full advantage
of its surface normals. We consider our work a crucial first step toward
real-time rendering of implicit neural representations under global
illumination.Comment: 9 pages, 8 figure
Ray Tracing Structured AMR Data Using ExaBricks
Structured Adaptive Mesh Refinement (Structured AMR) enables simulations to
adapt the domain resolution to save computation and storage, and has become one
of the dominant data representations used by scientific simulations; however,
efficiently rendering such data remains a challenge. We present an efficient
approach for volume- and iso-surface ray tracing of Structured AMR data on
GPU-equipped workstations, using a combination of two different data
structures. Together, these data structures allow a ray tracing based renderer
to quickly determine which segments along the ray need to be integrated and at
what frequency, while also providing quick access to all data values required
for a smooth sample reconstruction kernel. Our method makes use of the RTX ray
tracing hardware for surface rendering, ray marching, space skipping, and
adaptive sampling; and allows for interactive changes to the transfer function
and implicit iso-surfacing thresholds. We demonstrate that our method achieves
high performance with little memory overhead, enabling interactive high quality
rendering of complex AMR data sets on individual GPU workstations
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Asymptotic error of cubic B-spline interpolation using prefiltering
A popular class of reconstruction filters that are used in signal and image processing is based on cubic B-splines. One reason for their popularity is the fact that they can be efficiently implemented. This is specifically true with modern GPUs where cubic B-spline filtering can be implemented by means of linearly interpolating texture fetches so that the actual number of memory accesses can be significantly reduced. The curve obtained from filtering with the cubic B-spline does in general not interpolate the original data set. The latter can however be achieved by applying a prefiltering step that transforms the original data set. We study the asymptotic behavior of the reconstruction error of the cubic B-spline interpolation filter using a state of the art method that is based on a Taylor series expansion and that was carefully adjusted to accommodate the infinite support of this reconstruction filter
A Memory Efficient Encoding for Ray Tracing Large Unstructured Data
In theory, efficient and high-quality rendering of unstructured data should greatly benefit from modern GPUs, but in practice, GPUs are often limited by the large amount of memory that large meshes require for element representation and for sample reconstruction acceleration structures. We describe a memory-optimized encoding for large unstructured meshes that efficiently encodes both the unstructured mesh and corresponding sample reconstruction acceleration structure, while still allowing for fast random-access sampling as required for rendering. We demonstrate that for large data our encoding allows for rendering even the 2.9 billion element Mars Lander on a single off-the-shelf GPU-and the largest 6.3 billion version on a pair of such GPUs