VG-PBG08: Eurographics/IEEE VGTC on Volume and Point-Based Graphicshttps://diglib.eg.org:443/handle/10.2312/43292024-03-29T08:58:22Z2024-03-29T08:58:22ZSmooth Mixed-Resolution GPU Volume RenderingBeyer, JohannaHadwiger, MarkusMöller, TorstenFritz, Laurahttps://diglib.eg.org:443/handle/10.2312/VG.VG-PBG08.163-1702022-03-28T09:42:56Z2008-01-01T00:00:00ZSmooth Mixed-Resolution GPU Volume Rendering
Beyer, Johanna; Hadwiger, Markus; Möller, Torsten; Fritz, Laura
Hans-Christian Hege and David Laidlaw and Renato Pajarola and Oliver Staadt
We propose a mixed-resolution volume ray-casting approach that enables more flexibility in the choice of downsampling positions and filter kernels, allows freely mixing volume bricks of different resolutions during rendering, and does not require modifying the original sample values. A C0-continuous function is obtained everywhere with hardware-native filtering at full speed by simply warping texture coordinates of samples in transition regions. Additionally, we propose a simple but powerful, flat texture packing scheme that supports mixing different resolution levels in a single 3D volume cache texture with a very simple and fast address translation scheme. Although this packing constrains full scalability, it enables mixing different resolution levels in GPU-based ray-casting with only a single rendering pass. We demonstrate our approach on large real-world data, obtaining a continuous scalar function and shading at brick boundaries, using single-pass ray-casting at real-time frame rates.
2008-01-01T00:00:00ZMemory Efficient GPU-Based Ray Casting for Unstructured Volume RenderingMaximo, A.Ribeiro, S.Bentes, C.Oliveira, A.Farias, R.https://diglib.eg.org:443/handle/10.2312/VG.VG-PBG08.155-1622022-03-28T09:42:52Z2008-01-01T00:00:00ZMemory Efficient GPU-Based Ray Casting for Unstructured Volume Rendering
Maximo, A.; Ribeiro, S.; Bentes, C.; Oliveira, A.; Farias, R.
Hans-Christian Hege and David Laidlaw and Renato Pajarola and Oliver Staadt
Volume ray casting algorithms benefit greatly with recent increase of GPU capabilities and power. In this paper, we present a novel memory efficient ray casting algorithm for unstructured grids completely implemented on GPU using a recent off-the-shelf nVidia graphics card. Our approach is built upon a recent CPU ray casting algorithm, called VF-Ray, that considerably reduces the memory footprint while keeping good performance. In addition to the implementation of VF-Ray in the graphics hardware, we also propose a restructuring in its data structures. As a result, our algorithm is much faster than the original software version, while using significantly less memory, it needed only one-half of its previous memory usage. Comparing our GPU implementation to other hardware-based ray casting algorithms, our approach used between three to ten times less memory. These results made it possible for our GPU implementation to handle larger datasets on GPU than previous approaches.
2008-01-01T00:00:00ZPseudorandom Noise for Real-Time Volumetric Rendering of Fire in a Production SystemVanzine, Y.Vrajitoru, D.https://diglib.eg.org:443/handle/10.2312/VG.VG-PBG08.129-1362022-03-28T09:43:10Z2008-01-01T00:00:00ZPseudorandom Noise for Real-Time Volumetric Rendering of Fire in a Production System
Vanzine, Y.; Vrajitoru, D.
Hans-Christian Hege and David Laidlaw and Renato Pajarola and Oliver Staadt
This paper presents an effort at developing a robust, interactive framework for rendering 3D fire in real-time in a production environment. Many techniques of rendering fire in non real-time exist and are constantly employed by the movie industry and have directly influenced and inspired real-time fire rendering, including this paper. Macrolevel behavior of fire is characterized by wind fields, temperature and moving sources and is currently processed on the CPU while micro-level behavior like turbulence, flickering, separation and shape is created on the graphics hardware. This framework provides a set of tools for level designers to wield artistic and behavioral control over fire as part of the scene. The resulting system is able to scale well, to use as few processor cycles as possible, and to efficiently integrate into an existing production environment. We present performance statistics and assess the feasibility of achieving interactive frame rates within a 3D engine framework. The framerates we obtained vary from 42 to 168 depending on the rendering conditions, and indicate that the real-time procedural fire might not be far away.
2008-01-01T00:00:00ZAdaptive Sampling and Rendering of Fluids on the GPUZhang, YanciSolenthaler, BarbaraPajarola, Renatohttps://diglib.eg.org:443/handle/10.2312/VG.VG-PBG08.137-1462022-03-28T09:42:59Z2008-01-01T00:00:00ZAdaptive Sampling and Rendering of Fluids on the GPU
Zhang, Yanci; Solenthaler, Barbara; Pajarola, Renato
Hans-Christian Hege and David Laidlaw and Renato Pajarola and Oliver Staadt
In this paper, we propose a novel GPU-friendly algorithm for the Smoothed Particle Hydrodynamics (SPH) simulation for weakly compressible fluids. The major goal of our algorithm is to implement a GPU-based SPH simulation that can simulate and render a large number of particles at interactive speed. Additionally, our algorithm exhibits the following three features. Firstly, our algorithm supports adaptive sampling of the fluids. Particles can be split into several sub-particles in geometrically complex regions to provide a more accurate simulation. At the same time, nearby particles deep inside the fluids are merged to a single particle to reduce the number of particles. Secondly, the fluids are visualized by directly computing the intersection between ray and an isosurface defined by the surface particles. A dynamic particle grouping algorithm and equation solver are employed to quickly find the ray-isosurface intersection. Thirdly, based on the observation that the SPH simulation is a naturally parallel algorithm, the whole SPH simulation, including the adaptive sampling of the fluids as well as surface particle rendering, is executed on the GPU to fully utilize the computational power and parallelism of modern graphics hardware. Our experimental data shows that we can simulate about 50K adaptively sampled particles, or up to 120K particles in the fixed sampling case at a rate of approximately 20 time steps per second.
2008-01-01T00:00:00Z