Deep Volumetric Ambient Occlusion

Dominik Engel; Timo Ropinski

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Deep Volumetric Ambient Occlusion

机译：Deep Volumetric Ambient Occlusion

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摘要

We present a novel deep learning based technique for volumetric ambient occlusion in the context of direct volume rendering. Our proposed Deep Volumetric Ambient Occlusion (DVAO) approach can predict per-voxel ambient occlusion in volumetric data sets, while considering global information provided through the transfer function. The proposed neural network only needs to be executed upon change of this global information, and thus supports real-time volume interaction. Accordingly, we demonstrate DVAO's ability to predict volumetric ambient occlusion, such that it can be applied interactively within direct volume rendering. To achieve the best possible results, we propose and analyze a variety of transfer function representations and injection strategies for deep neural networks. Based on the obtained results we also give recommendations applicable in similar volume learning scenarios. Lastly, we show that DVAO generalizes to a variety of modalities, despite being trained on computed tomography data only.

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《IEEE transactions on visualization and computer graphics》 |2021年第2期|1268-1278|共11页
作者
Dominik Engel; Timo Ropinski;
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作者单位

Ulm University;

Ulm University and Link?ping University, Norrk?ping;

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原文格式 PDF
正文语种英语
中图分类模式识别与装置;
关键词
Rendering (computer graphics); Transfer functions; Lighting; Training; Three-dimensional displays; Neural networks; Deep learning; Volume illumination; deep learning; direct volume rendering;

Deep Volumetric Ambient Occlusion

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