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.

翻译：我们提出了一种基于神经网络的实时体渲染方法，用于逼真且高效地渲染体积介质。传统体渲染方法通过路径追踪求解辐射传输方程，计算量巨大且无法实现实时渲染。因此，本文采用神经网络模拟求解辐射传输方程的迭代积分过程，以加速体积介质的体渲染。具体而言，本文首先对体积介质进行数据处理，生成包含密度特征、透射率特征和相位特征在内的多种采样特征。将分层透射率场输入3D-CNN网络，以计算更重要的透射率特征。其次，利用漫反射采样模板和高光采样模板将三类采样特征分层输入网络。该方法能够更关注光散射、高光和阴影，并通过注意力模块选择重要的通道特征。最后，通过主干网络预测所有采样模板中心点的散射分布。该方法可实现逼真的体积介质渲染效果，并在保持渲染质量的同时大幅提升渲染速度，这对实时渲染应用具有重要意义。实验结果表明，我们的方法优于以往方法。