819 research outputs found
TetSplat: Real-time Rendering and Volume Clipping of Large Unstructured Tetrahedral Meshes
We present a novel approach to interactive visualization and exploration of large unstructured tetrahedral meshes. These massive 3D meshes are used in mission-critical CFD and structural mechanics simulations, and typically sample multiple field values on several millions of unstructured grid points. Our method relies on the pre-processing of the tetrahedral mesh to partition it into non-convex boundaries and internal fragments that are subsequently encoded into compressed multi-resolution data representations. These compact hierarchical data structures are then adaptively rendered and probed in real-time on a commodity PC. Our point-based rendering algorithm, which is inspired by QSplat, employs a simple but highly efficient splatting technique that guarantees interactive frame-rates regardless of the size of the input mesh and the available rendering hardware. It furthermore allows for real-time probing of the volumetric data-set through constructive solid geometry operations as well as interactive editing of color transfer functions for an arbitrary number of field values. Thus, the presented visualization technique allows end-users for the first time to interactively render and explore very large unstructured tetrahedral meshes on relatively inexpensive hardware
RT-kNNS Unbound: Using RT Cores to Accelerate Unrestricted Neighbor Search
The problem of identifying the k-Nearest Neighbors (kNNS) of a point has
proven to be very useful both as a standalone application and as a subroutine
in larger applications. Given its far-reaching applicability in areas such as
machine learning and point clouds, extensive research has gone into leveraging
GPU acceleration to solve this problem. Recent work has shown that using Ray
Tracing cores in recent GPUs to accelerate kNNS is much more efficient compared
to traditional acceleration using shader cores. However, the existing
translation of kNNS to a ray tracing problem imposes a constraint on the search
space for neighbors. Due to this, we can only use RT cores to accelerate
fixed-radius kNNS, which requires the user to set a search radius a priori and
hence can miss neighbors. In this work, we propose TrueKNN, the first unbounded
RT-accelerated neighbor search. TrueKNN adopts an iterative approach where we
incrementally grow the search space until all points have found their k
neighbors. We show that our approach is orders of magnitude faster than
existing approaches and can even be used to accelerate fixed-radius neighbor
searches.Comment: This paper has been accepted at the International Conference on
Supercomputing 2023 (ICS'23
Deep Hierarchical Super-Resolution for Scientific Data Reduction and Visualization
We present an approach for hierarchical super resolution (SR) using neural
networks on an octree data representation. We train a hierarchy of neural
networks, each capable of 2x upscaling in each spatial dimension between two
levels of detail, and use these networks in tandem to facilitate large scale
factor super resolution, scaling with the number of trained networks. We
utilize these networks in a hierarchical super resolution algorithm that
upscales multiresolution data to a uniform high resolution without introducing
seam artifacts on octree node boundaries. We evaluate application of this
algorithm in a data reduction framework by dynamically downscaling input data
to an octree-based data structure to represent the multiresolution data before
compressing for additional storage reduction. We demonstrate that our approach
avoids seam artifacts common to multiresolution data formats, and show how
neural network super resolution assisted data reduction can preserve global
features better than compressors alone at the same compression ratios
Mobile graphics: SIGGRAPH Asia 2017 course
Peer ReviewedPostprint (published version
Time-varying volume visualization
Volume rendering is a very active research field in Computer Graphics because of its wide range of applications in various sciences, from medicine to flow mechanics. In this report, we survey a state-of-the-art on time-varying volume rendering. We state several basic concepts and then we establish several criteria to classify the studied works: IVR versus DVR, 4D versus 3D+time, compression techniques, involved architectures, use of parallelism and image-space versus object-space coherence. We also address other related problems as transfer functions and 2D cross-sections computation of time-varying volume data. All the papers reviewed are classified into several tables based on the mentioned classification and, finally, several conclusions are presented.Preprin
Generalized Neighbor Search using Commodity Hardware Acceleration
Tree-based Nearest Neighbor Search (NNS) is hard to parallelize on GPUs.
However, newer Nvidia GPUs are equipped with Ray Tracing (RT) cores that can
build a spatial tree called Bounding Volume Hierarchy (BVH) to accelerate
graphics rendering. Recent work proposed using RT cores to implement NNS, but
they all have a hardware-imposed constraint on the type of distance metric,
which is the Euclidean distance. We propose and implement two approaches for
generalized distance computations: filter-refine, and monotone transformation,
each of which allows non-euclidean nearest neighbor queries to be performed in
terms of Euclidean distances. We find that our reductions improve the time
taken to perform distance computations during the search, thereby improving the
overall performance of the NNS
Visual Data Representation using Context-Aware Samples
The rapid growth in the complexity of geometry models has necessisated revision of several conventional techniques in computer graphics. At the heart of this trend is the representation of geometry with locally constant approximations using independent sample primitives. This generally leads to a higher sampling rate and thus a high cost of representation, transmission, and rendering. We advocate an alternate approach involving context-aware samples that capture the local variation of the geometry. We detail two approaches; one, based on differential geometry and the other based on statistics. Our differential-geometry-based approach captures the context of the local geometry using an estimation of the local Taylor's series expansion. We render such samples using programmable Graphics Processing Unit (GPU) by fast approximation of the geometry in the screen space. The benefits of this representation can also be seen in other applications such as simulation of light transport. In our statistics-based approach we capture the context of the local geometry using Principal Component Analysis (PCA). This allows us to achieve hierarchical detail by modeling the geometry in a non-deterministic fashion as a hierarchical probability distribution. We approximate the geometry and its attributes using quasi-random sampling. Our results show a significant rendering speedup and savings in the geometric bandwidth when compared to current approaches
Towards a High Quality Real-Time Graphics Pipeline
Modern graphics hardware pipelines create photorealistic images with high geometric complexity in real time. The quality is constantly improving and advanced techniques from feature film visual effects, such as high dynamic range images and support for higher-order surface primitives, have recently been adopted. Visual effect techniques have large computational costs and significant memory bandwidth usage. In this thesis, we identify three problem areas and propose new algorithms that increase the performance of a set of computer graphics techniques. Our main focus is on efficient algorithms for the real-time graphics pipeline, but parts of our research are equally applicable to offline rendering. Our first focus is texture compression, which is a technique to reduce the memory bandwidth usage. The core idea is to store images in small compressed blocks which are sent over the memory bus and are decompressed on-the-fly when accessed. We present compression algorithms for two types of texture formats. High dynamic range images capture environment lighting with luminance differences over a wide intensity range. Normal maps store perturbation vectors for local surface normals, and give the illusion of high geometric surface detail. Our compression formats are tailored to these texture types and have compression ratios of 6:1, high visual fidelity, and low-cost decompression logic. Our second focus is tessellation culling. Culling is a commonly used technique in computer graphics for removing work that does not contribute to the final image, such as completely hidden geometry. By discarding rendering primitives from further processing, substantial arithmetic computations and memory bandwidth can be saved. Modern graphics processing units include flexible tessellation stages, where rendering primitives are subdivided for increased geometric detail. Images with highly detailed models can be synthesized, but the incurred cost is significant. We have devised a simple remapping technique that allowsfor better tessellation distribution in screen space. Furthermore, we present programmable tessellation culling, where bounding volumes for displaced geometry are computed and used to conservatively test if a primitive can be discarded before tessellation. We introduce a general tessellation culling framework, and an optimized algorithm for rendering of displaced BĂ©zier patches, which is expected to be a common use case for graphics hardware tessellation. Our third and final focus is forward-looking, and relates to efficient algorithms for stochastic rasterization, a rendering technique where camera effects such as depth of field and motion blur can be faithfully simulated. We extend a graphics pipeline with stochastic rasterization in spatio-temporal space and show that stochastic motion blur can be rendered with rather modest pipeline modifications. Furthermore, backface culling algorithms for motion blur and depth of field rendering are presented, which are directly applicable to stochastic rasterization. Hopefully, our work in this field brings us closer to high quality real-time stochastic rendering
R-LODs: fast LOD-based ray tracing of massive models
We present a novel LOD (level-of-detail) algorithm to accelerate ray tracing of massive models. Our approach computes drastic simplifications of the model and the LODs are well integrated with the kd-tree data structure. We introduce a simple and efficient LOD metric to bound the error for primary and secondary rays. The LOD representation has small runtime overhead and our algorithm can be combined with ray coherence techniques and cache-coherent layouts to improve the performance. In practice, the use of LODs can alleviate aliasing artifacts and improve memory coherence. We implement our algorithm on both 32bit and 64bit machines and able to achieve up to 2-20 times improvement in frame rate of rendering models consisting of tens or hundreds of millions of triangles with little loss in image quality
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