Engel Dominik, Ropinski Timo
IEEE Trans Vis Comput Graph. 2021 Feb;27(2):1268-1278. doi: 10.1109/TVCG.2020.3030344. Epub 2021 Jan 28.
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.
我们提出了一种基于深度学习的新技术，用于在直接体绘制的背景下进行体环境光遮蔽。我们提出的深度体环境光遮蔽（DVAO）方法可以预测体数据集中每个体素的环境光遮蔽，同时考虑通过传递函数提供的全局信息。所提出的神经网络只需要在这种全局信息发生变化时执行，因此支持实时体交互。相应地，我们展示了DVAO预测体环境光遮蔽的能力，使其能够在直接体绘制中进行交互式应用。为了获得尽可能好的结果，我们为深度神经网络提出并分析了各种传递函数表示和注入策略。基于获得的结果，我们还给出了适用于类似体学习场景的建议。最后，我们表明，尽管DVAO仅在计算机断层扫描数据上进行训练，但它可以推广到多种模态。
