We present ETER, an elastic tessellation framework for rendering large-scale NURBS models with pixel-accurate and crack-free quality at real-time frame rates. We propose a highly parallel adaptive tessellation algorit...
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We present ETER, an elastic tessellation framework for rendering large-scale NURBS models with pixel-accurate and crack-free quality at real-time frame rates. We propose a highly parallel adaptive tessellation algorithm to achieve pixel accuracy, measured by the screen space error between the exact surface and its triangulation. To resolve a bottleneck in NURBS rendering, we present a novel evaluation method based on uniform sampling grids and accelerated by gpu Tensor Cores. Compared to evaluation based on hardware tessellation, our method has achieved a significant speedup of 2.9 to 16.2 times depending on the degrees of the patches. We develop an efficient crack-filling algorithm based on conservative rasterization and visibility buffer to fill the tessellation-induced cracks while greatly reducing the jagged effect introduced by conservative rasterization. We integrate all our novel algorithms, implemented in CUDA, into a gpu NURBS rendering pipeline based on Mesh Shaders and hybrid software/hardware rasterization. Our performance data on a commodity gpu show that the rendering pipeline based on ETER is capable of rendering up to 3.7 million patches (0.25 billion tessellated triangles) in real-time (30FPS). With its advantages in performance, scalability, and visual quality in rendering large-scale NURBS models, a real-time tessellation solution based on ETER can be a powerful alternative or even a potential replacement for the existing pre-tessellation solution in CAD systems.
We introduce here a maiden problem of bimodal isotropic remeshing and present a gpu-based algorithm for the same. It takes as input a triangulated mesh (orientable 2-manifold closed surface) and produces an output mes...
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ISBN:
(纸本)9783031451690;9783031451706
We introduce here a maiden problem of bimodal isotropic remeshing and present a gpu-based algorithm for the same. It takes as input a triangulated mesh (orientable 2-manifold closed surface) and produces an output mesh, subject to the following constraints: (i) the Hausdorff error between the input surface and the output surface is within a user-specified value;(ii) the constituent triangles of the output mesh are bimodal and isotropic, i.e., the triangles are almost equilateral in shape and have predominantly two well-separated sizes-the bigger triangles to fit low-curvature regions and the smaller ones to fit high-curvature regions. In order to parallelize the entire process, we use voxel processing. Our algorithm incorporates a novel concept of bimodal curvature map based on Poisson characteristics of discrete curvature. From this map, the sizes of the triangles are automatically determined and then a curvature-adaptive centroidal Voronoi tessellation, followed by Delaunay triangulation, is done to obtain the desired output. Experimental results on different models are found to be encouraging, and future extensions of the proposed concept are discussed at the end.
We propose a novel algorithm for low-poly remeshing of 3D surfaces that runs fully in gpu. Since the input mesh is generally not well-organized, performing mesh simplification directly on the input mesh is liable to p...
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ISBN:
(纸本)9781450398220
We propose a novel algorithm for low-poly remeshing of 3D surfaces that runs fully in gpu. Since the input mesh is generally not well-organized, performing mesh simplification directly on the input mesh is liable to produce a low-poly mesh with a compromised face quality. Hence, instead of doing that, we voxelize the input mesh first. On the voxelized surface, we perform curvature estimation and discretization, Centroidal Voronoi Tessellation (CVT), and Delaunay triangulation to generate the output mesh. To control the vertex count, we exploit the curvature map, such that sufficiently large triangles constitute the low-curvature regions, whereas appropriately small triangles build up the high-curvature portions. We produce low-poly meshes at various levels of detail and measure their quality using Hausdorff error. We test our algorithm on different 3D scenes, and they are particularly chosen because they contain multiple objects with varied topologies. Upon testing on these datasets, our algorithm successfully achieves very low-poly approximation with a reasonable mesh quality, and at a reasonably faster pace, thus establishing its efficacy for mesh simplification.
This work presents strategies to massively parallelize recursive filters on inputs of one dimension (1D) or three dimensions (3D), complementing and improving on previous state-of-the-art algorithms on two dimensions ...
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This work presents strategies to massively parallelize recursive filters on inputs of one dimension (1D) or three dimensions (3D), complementing and improving on previous state-of-the-art algorithms on two dimensions (2D). Each strategy is reusable on different algorithms for parallel processing with feedback data dependencies, allowing to develop highly optimized algorithms for computing digital filters in general, with double-pass causal-anticausal feedbacks, in one or multiple dimensions. The algorithms are linear in time and memory, exposes a high number of parallel tasks, and they are implemented on graphics processing units, i.e. gpus. One major barrier in this area is to have such algorithms faster than generic counterparts in available libraries, and another is to have them in an easy-to-use manner. To overcome the latter, the implementation of the presented strategies is available as open source, and, to overcome the former, timing performance and comparison results are provided, including a range of publicly available source codes and libraries, showing that this work outperforms fastest prior algorithms. (C) 2021 Elsevier Inc. All rights reserved.
Locating micro-seismic events is of utmost importance in seismic exploration, especially when searching for unconventional oil and gas resources. The arrival-time-difference approach is the dominant source location me...
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Locating micro-seismic events is of utmost importance in seismic exploration, especially when searching for unconventional oil and gas resources. The arrival-time-difference approach is the dominant source location method currently used in the field of micro-seismic source location. However, micro-seismic events can be generated by any arbitrary rock movement and are often accompanied by interference noise. Recordings show characteristics of complicated wavelets and low signal-to-noise ratios. Under such conditions, conventional triangulation methods may have difficulty producing reliable locations;time-reversal imaging micro-seismic event location techniques are more promising. Locating micro-seismic events must be performed on-site for real-time monitoring of hydraulic fracturing. Introducing wave equation imaging techniques when locating micro-seismic events will increase the computation time, thus complicating real-time site monitoring. Therefore, the use of graphics processing unit (gpu) devices to accelerate time-reversal imaging micro-seismic event location technology becomes imperative. Three-dimensional synthetic data examples have demonstrated that the gpu-based time-reversal imaging micro-seismic event location method is typically 18 times faster than the central processing unit (CPU)-based implementation. The performance boost afforded by the GPO architecture allows us to locate micro-seismic events in 3D at a lower hardware cost and in less time than has been previously possible. (C) 2015 Elsevier Ltd. All rights reserved.
Locating micro-seismic events is of utmost importance in seismic exploration,especially when searching for unconventional oil and gas *** arrival-time-difference approach is the dominant source location method current...
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Locating micro-seismic events is of utmost importance in seismic exploration,especially when searching for unconventional oil and gas *** arrival-time-difference approach is the dominant source location method currently used in the field of micro-seismic source ***,micro-seismic events can be generated by any arbitrary rods movement and are often accompanied by interference *** show characteristics of complicated wavelets and low signal-to-noise *** such conditions,conventional triangulation methods may have difficulty producing reliable locations;time-reversal imaging micro-seismic event location techniques are more *** microseismic events must be performed on-site for real-time monitoring of hydraulic *** wave equation imaging techniques when locating micro-seismic events will increase the computation time,thus complicating real-time site ***,the use of graphics processing unit(gpu)devices to accelerate time-reversal imaging micro-seismic event location technology becomes ***-dimensional synthetic data examples have demonstrated that the gpu-based time-reversal imaging micro-seismic event location method is typically 18 times faster than the central processing unit(CPU)-based *** performance boost afforded by the gpu architecture allows us to locate micro-seismic events in 3D at a lower hardware cost and in less time than has been previously possible.
An efficient algorithm for time-domain solution of the acoustic wave equation for the purpose of room acoustics is presented. It is based on adaptive rectangular decomposition of the scene and uses analytical solution...
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An efficient algorithm for time-domain solution of the acoustic wave equation for the purpose of room acoustics is presented. It is based on adaptive rectangular decomposition of the scene and uses analytical solutions within the partitions that rely on spatially invariant speed of sound. This technique is suitable for auralizations and sound field visualizations, even on coarse meshes approaching the Nyquist limit. It is demonstrated that by carefully mapping all components of the algorithm to match the parallel processing capabilities of graphics processors (gpus), significant improvement in performance is gained compared to the corresponding CPU-based solver, while maintaining the numerical accuracy. Substantial performance gain over a high-order finite-difference time-domain method is observed. Using this technique, a 1 s long simulation can be performed on scenes of air volume 7500 m(3) till 1650 Hz within 18 min compared to the corresponding CPU-based solver that takes around 5 h and a high-order finite-difference time-domain solver that could take up to three weeks on a desktop computer. To the best of the authors' knowledge, this is the fastest time-domain solver for modeling the room acoustics of large, complex-shaped 3D scenes that generates accurate results for both auralization and visualization. (C) 2011 Elsevier Ltd. All rights reserved.
As there is no hardware support neither for rendering trimmed NURBS-the standard surface representation in CAD-nor for T-Spline surfaces the usability of existing rendering APIs like OpenGL, where a run-time tessellat...
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As there is no hardware support neither for rendering trimmed NURBS-the standard surface representation in CAD-nor for T-Spline surfaces the usability of existing rendering APIs like OpenGL, where a run-time tessellation is performed on the CPU, is limited to simple scenes. Due to the irregular mesh data structures required for trimming no algorithms exists that exploit the gpu for tessellation. Therefore, recent approaches perform a pretessellation and use level-of-detail techniques. In contrast to a simple API these methods require tedious preparation of the models before rendering and hinder interactive editing. Furthermore, due to the tremendous amount of triangle data smooth zoom-ins from long shot to close-up are not possible. In this paper we show how the trimming region can be defined by a trim-texture that is dynamically adapted to the required resolution and allows for an efficient trimming of surfaces on the gpu. Combining this new method with gpu-based tessellation of cubic rational surfaces allows a new rendering algorithm for arbitrary trimmed NURBS and T-Spline surfaces with prescribed error in screen space on the gpu. The performance exceeds current CPU-based techniques by a factor of up to 1000 and makes real-time visualization of real-world trimmed NURBS and T-Spline models possible on consumer-level graphics cards.
As there is no hardware support neither for rendering trimmed NURBS-the standard surface representation in CAD-nor for T-Spline surfaces the usability of existing rendering APIs like OpenGL, where a run-time tessellat...
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ISBN:
(纸本)9781450378277
As there is no hardware support neither for rendering trimmed NURBS-the standard surface representation in CAD-nor for T-Spline surfaces the usability of existing rendering APIs like OpenGL, where a run-time tessellation is performed on the CPU, is limited to simple scenes. Due to the irregular mesh data structures required for trimming no algorithms exists that exploit the gpu for tessellation. Therefore, recent approaches perform a pretessellation and use level-of-detail techniques. In contrast to a simple API these methods require tedious preparation of the models before rendering and hinder interactive editing. Furthermore, due to the tremendous amount of triangle data smooth zoom-ins from long shot to close-up are not possible. In this paper we show how the trimming region can be defined by a trim-texture that is dynamically adapted to the required resolution and allows for an efficient trimming of surfaces on the gpu. Combining this new method with gpu-based tessellation of cubic rational surfaces allows a new rendering algorithm for arbitrary trimmed NURBS and T-Spline surfaces with prescribed error in screen space on the gpu. The performance exceeds current CPU-based techniques by a factor of up to 1000 and makes real-time visualization of real-world trimmed NURBS and T-Spline models possible on consumer-level graphics cards.
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