  We propose a neural network-based real-time volume rendering method for
realistic and efficient rendering of volumetric media. The traditional volume
rendering method uses path tracing to solve the radiation transfer equation,
which requires a huge amount of calculation and cannot achieve real-time
rendering. Therefore, this paper uses neural networks to simulate the iterative
integration process of solving the radiative transfer equation to speed up the
volume rendering of volume media. Specifically, the paper first performs data
processing on the volume medium to generate a variety of sampling features,
including density features, transmittance features and phase features. The
hierarchical transmittance fields are fed into a 3D-CNN network to compute more
important transmittance features. Secondly, the diffuse reflection sampling
template and the highlight sampling template are used to layer the three types
of sampling features into the network. This method can pay more attention to
light scattering, highlights and shadows, and then select important channel
features through the attention module. Finally, the scattering distribution of
the center points of all sampling templates is predicted through the backbone
neural network. This method can achieve realistic volumetric media rendering
effects and greatly increase the rendering speed while maintaining rendering
quality, which is of great significance for real-time rendering applications.
Experimental results indicate that our method outperforms previous methods.
