Hybrid Sample-based Surface Rendering

Reichl, Florian; Chajdas, Matthäus G.; Burger, K.; Westermann, Rüdiger

doi:10.2312/pe/vmv/vmv12/047-054

Cited by 2 publications

(1 citation statement)

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“…For isosurface ray-casting, frame to frame depth buffer coherence on the GPU was employed to speed up first-hit determination [5,27]. A number of approaches have shown the efficiency of GPU volume ray-casting when paired with compact isosurface representations, brick-based or octree subdivision, and out-of-core strategies for handling data sets too large to be stored on the GPU [11,13,40,50]. For a thorough overview of GPU approaches for large-scale volume rendering, let us refer to the report by Beyer et al [3].…”

Section: Related Workmentioning

confidence: 99%

Volumetric Isosurface Rendering with Deep Learning-Based Super-Resolution

Weiß

Chu

Thuerey

et al. 2021

IEEE Trans. Visual. Comput. Graphics

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a) (b) (c) Fig. 1: Our super-resolution network can upscale (a) an input sampling of isosurface normals and depths at low resolution (i.e., 320x240), to (b) a high-resolution normal and depth map (i.e., 1280x960) with ambient occlusion. For ease of interpretation, only the shaded output is shown. (c) The ground truth is rendered at 1280x960. Samples are from a 1024 3 grid, ground truth renders at 0.16 and 18.6 secs w/ and w/o ambient occlusion, super-resolution takes 0.07 sec Abstract-Rendering an accurate image of an isosurface in a volumetric field typically requires large numbers of data samples. Reducing the number of required samples lies at the core of research in volume rendering. With the advent of deep learning networks, a number of architectures have been proposed recently to infer missing samples in multi-dimensional fields, for applications such as image super-resolution and scan completion.In this paper, we investigate the use of such architectures for learning the upscaling of a low-resolution sampling of an isosurface to a higher resolution, with high fidelity reconstruction of spatial detail and shading. We introduce a fully convolutional neural network, to learn a latent representation generating a smooth, edge-aware normal field and ambient occlusions from a low-resolution normal and depth field. By adding a frame-to-frame motion loss into the learning stage, the upscaling can consider temporal variations and achieves improved frame-to-frame coherence. We demonstrate the quality of the network for isosurfaces which were never seen during training, and discuss remote and in-situ visualization as well as focus+context visualization as potential applications.

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Section: Related Workmentioning

confidence: 99%

Volumetric Isosurface Rendering with Deep Learning-Based Super-Resolution

Weiß

Chu

Thuerey

et al. 2021

IEEE Trans. Visual. Comput. Graphics

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Memory-Efficient On-the-Fly Voxelization and Rendering of Particle Data

Zirr

Dachsbacher

2018

IEEE Trans. Visual. Comput. Graphics

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In this paper we present a novel GPU-friendly real-time voxelization technique for rendering homogeneous media that is defined by particles, e.g., fluids obtained from particle-based simulations such as Smoothed Particle Hydrodynamics (SPH). Our method computes view-adaptive binary voxelizations with on-the-fly compression of a tiled perspective voxel grid, achieving higher resolutions than previous approaches. It allows for interactive generation of realistic images, enabling advanced rendering techniques such as ray casting-based refraction and reflection, light scattering and absorption, and ambient occlusion. In contrast to previous methods, it does not rely on preprocessing such as expensive, and often coarse, scalar field conversion or mesh generation steps. Our method directly takes unsorted particle data as input. It can be further accelerated by identifying fully populated simulation cells during simulation. The extracted surface can be filtered to achieve smooth surface appearance. Finally, we provide a new scheme for accelerated ray casting inside the voxelization.

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Memory-Efficient On-The-Fly Voxelization of Particle Data

Zirr

Dachsbacher

2015

Eurographics Symposium on Parallel Graphics and Visualization

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Hybrid Sample-based Surface Rendering

Cited by 2 publications

References 0 publications

Volumetric Isosurface Rendering with Deep Learning-Based Super-Resolution

Volumetric Isosurface Rendering with Deep Learning-Based Super-Resolution

Memory-Efficient On-the-Fly Voxelization and Rendering of Particle Data

Memory-Efficient On-The-Fly Voxelization of Particle Data

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