论文标题
深容量的环境阻塞
Deep Volumetric Ambient Occlusion
论文作者
论文摘要
我们提出了一种新型的基于深度学习的技术，用于在直接体积渲染的背景下进行体积环境阻塞。我们提出的深容量环境阻塞（DVAO）方法可以预测体积数据集中的每个体腔环境闭塞，同时考虑通过传输函数提供的全局信息。所提出的神经网络只需要在更改此全局信息时才能执行，因此支持实时卷相互作用。因此，我们证明了DVAO可以预测体积环境阻塞的能力，因此可以在直接体积渲染中进行交互应用。为了获得最佳的结果，我们建议和分析深层神经网络的各种传递函数表示和注射策略。根据获得的结果，我们还提供了适用于类似量学习方案的建议。最后，我们表明，尽管仅在计算机断层扫描数据上接受培训，但DVAO概括了各种方式。
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 DVAOs 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.
