Deep learning

Electrical engineering

Physics

Data visualization

Volume rendering

Pipeline (software)

Transfer function

Computer graphics (images)

Engineering

Artificial intelligence

Programming language

Volume (thermodynamics)

Rendering (computer graphics)

Generative adversarial network

Visualization

Computer science

Quantum mechanics

Deep neural networks

Computer vision

Artificial neural network

Convolutional neural network

In this work, we propose a novel approach of volume visualization without explicit traditional rendering pipeline. In our proposed method, volumetric images can be interactively `reversed' given the volumetric data and a static volume rendered image under the desired rendering effect. Our pipeline enables 3D-navigation on it for exploring the given volumetric data without explicit transfer function. In our approach, deep neural networks, combined usage of Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNN) are employed to synthesize high-resolution and perceptually authentic images directly, inheriting the desired transfer function and viewing parameter implicitly given by the input images respectively.

DNN-VolVis: Interactive Volume Visualization Supported by Deep Neural Network