67 research outputs found
GPU-based Streaming for Parallel Level of Detail on Massive Model Rendering
Rendering massive 3D models in real-time has long been recognized as a very challenging problem because of the limited computational power and memory space available in a workstation. Most existing rendering techniques, especially level of detail (LOD) processing, have suffered from their sequential execution natures, and does not scale well with the size of the models. We present a GPU-based progressive mesh simplification approach which enables the interactive rendering of large 3D models with hundreds of millions of triangles. Our work contributes to the massive rendering research in two ways. First, we develop a novel data structure to represent the progressive LOD mesh, and design a parallel mesh simplification algorithm towards GPU architecture. Second, we propose a GPU-based streaming approach which adopt a frame-to-frame coherence scheme in order to minimize the high communication cost between CPU and GPU. Our results show that the parallel mesh simplification algorithm and GPU-based streaming approach significantly improve the overall rendering performance
Shape from Projections via Differentiable Forward Projector for Computed Tomography
In computed tomography, the reconstruction is typically obtained on a voxel
grid. In this work, however, we propose a mesh-based reconstruction method. For
tomographic problems, 3D meshes have mostly been studied to simulate data
acquisition, but not for reconstruction, for which a 3D mesh means the inverse
process of estimating shapes from projections. In this paper, we propose a
differentiable forward model for 3D meshes that bridge the gap between the
forward model for 3D surfaces and optimization. We view the forward projection
as a rendering process, and make it differentiable by extending recent work in
differentiable rendering. We use the proposed forward model to reconstruct 3D
shapes directly from projections. Experimental results for single-object
problems show that the proposed method outperforms traditional voxel-based
methods on noisy simulated data. We also apply the proposed method on electron
tomography images of nanoparticles to demonstrate the applicability of the
method on real data
A sparse octree gravitational N-body code that runs entirely on the GPU processor
We present parallel algorithms for constructing and traversing sparse octrees
on graphics processing units (GPUs). The algorithms are based on parallel-scan
and sort methods. To test the performance and feasibility, we implemented them
in CUDA in the form of a gravitational tree-code which completely runs on the
GPU.(The code is publicly available at:
http://castle.strw.leidenuniv.nl/software.html) The tree construction and
traverse algorithms are portable to many-core devices which have support for
CUDA or OpenCL programming languages. The gravitational tree-code outperforms
tuned CPU code during the tree-construction and shows a performance improvement
of more than a factor 20 overall, resulting in a processing rate of more than
2.8 million particles per second.Comment: Accepted version. Published in Journal of Computational Physics. 35
pages, 12 figures, single colum
Fast, effective BVH updates for dynamic ray-traced scenes using tree rotations
technical reportBounding volume hierarchies are a popular choice for ray tracing animated scenes due to the relative simplicity of refitting bounding volumes around moving geometry. However, the quality of such a refitted tree can degrade rapidly if objects in the scene deform or rearrange significantly as the animation progresses, resulting in dramatic increases in rendering times. Existing solutions involve occasional or heuristically triggered rebuilds of the BVH to reduce this effect. In this work, we describe how to efficiently extend refitting with local restructuring operations called tree rotations which can mitigate the effects that moving primitives have on BVH quality by rearranging nodes in the tree during each refit rather than triggering a full rebuild. The result is a fast, lightweight, incremental update algorithm that requires negligible memory, has minor update times and parallelizes easily, yet avoids significant degradation in tree quality or the need for rebuilding while maintaining fast rendering times. We show that our method approaches or exceeds the frame rates of other techniques and is consistently among the best options regardless of the animation scene
A scalable H-matrix approach for the solution of boundary integral equations on multi-GPU clusters
In this work, we consider the solution of boundary integral equations by
means of a scalable hierarchical matrix approach on clusters equipped with
graphics hardware, i.e. graphics processing units (GPUs). To this end, we
extend our existing single-GPU hierarchical matrix library hmglib such that it
is able to scale on many GPUs and such that it can be coupled to arbitrary
application codes. Using a model GPU implementation of a boundary element
method (BEM) solver, we are able to achieve more than 67 percent relative
parallel speed-up going from 128 to 1024 GPUs for a model geometry test case
with 1.5 million unknowns and a real-world geometry test case with almost 1.2
million unknowns. On 1024 GPUs of the cluster Titan, it takes less than 6
minutes to solve the 1.5 million unknowns problem, with 5.7 minutes for the
setup phase and 20 seconds for the iterative solver. To the best of the
authors' knowledge, we here discuss the first fully GPU-based
distributed-memory parallel hierarchical matrix Open Source library using the
traditional H-matrix format and adaptive cross approximation with an
application to BEM problems
An Investigation into Animating Plant Structures within Real-time Constraints
This paper is an analysis of current developments in rendering botanical structures for scientic and entertainment purposes with a focus on visualising growth. The choices of practical investigations produce a novel approach for parallel parsing of difficult bracketed L-Systems, based upon the work of Lipp, Wonka and Wimmer (2010). Alongside this is a general overview of the issues involved when looking at growing systems, technical details involving programming for the Graphics Processing Unit (GPU) and other possible solutions for further work that also could achieve the project's goals
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