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.

提出了一种基于神经网络的实时体积渲染方法，用于逼真高效地渲染体积媒体，通过模拟迭代积分过程来加速体积媒体的渲染速度，并在保持渲染质量的同时实现逼真的效果，实验结果表明该方法优于先前的方法。

多散射高反射异性材料的实时渲染方法

A real-time rendering method for high albedo anisotropic materials with  multiple scattering

Volumetric media, popularly known as holograms, need to be delivered to users
using both on-demand and live streaming, for new augmented reality (AR) and
virtual reality (VR) experiences. As in video streaming, hologram streaming
must support network adaptivity and fast startup, but must also moderate large
bandwidths, multiple simultaneously streaming objects, and frequent user
interaction, which requires low delay. In this paper, we introduce the first
system to our knowledge designed specifically for streaming volumetric media.
The system reduces bandwidth by introducing 3D tiles, and culling them or
reducing their level of detail depending on their relation to the user's view
frustum and distance to the user. Our system reduces latency by introducing a
window-based buffer, which in contrast to a queue-based buffer allows
insertions near the head of the buffer rather than only at the tail of the
buffer, to respond quickly to user interaction. To allocate bits between
different tiles across multiple objects, we introduce a simple greedy yet
provably optimal algorithm for rate-utility optimization. We introduce utility
measures based not only on the underlying quality of the representation, but on
the level of detail relative to the user's viewpoint and device resolution.
Simulation results show that the proposed algorithm provides superior quality
compared to existing video-streaming approaches adapted to hologram streaming,
in terms of utility and user experience over variable, throughput-constrained
networks.

该研究介绍了一种专门用于流式传输全息媒体的系统，通过引入 3D 切片并根据其与用户视野的关系和距离减少它们的数量或减少其细节级别来降低带宽和延迟，提高用户体验。