Deep Volumetric Ambient Occlusion
This work addresses the challenge of efficient ambient occlusion for volume rendering, which is incremental as it applies deep learning to a known bottleneck in visualization.
The paper tackles the problem of real-time volumetric ambient occlusion in direct volume rendering by introducing Deep Volumetric Ambient Occlusion (DVAO), a deep learning technique that predicts per-voxel ambient occlusion with global transfer function information, achieving interactive performance and generalization across modalities despite training only on computed tomography data.
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.