Volume data is commonly found in many scientific disciplines, like medicine,
physics, and biology. Experts rely on robust scientific visualization
techniques to extract valuable insights from the data. Recent years have shown
path tracing to be the preferred approach for volumetric rendering, given its
high levels of realism. However, real-time volumetric path tracing often
suffers from stochastic noise and long convergence times, limiting interactive
exploration. In this paper, we present a novel method to enable real-time
global illumination for volume data visualization. We develop Photon Field
Networks -- a phase-function-aware, multi-light neural representation of
indirect volumetric global illumination. The fields are trained on multi-phase
photon caches that we compute a priori. Training can be done within seconds,
after which the fields can be used in various rendering tasks. To showcase
their potential, we develop a custom neural path tracer, with which our photon
fields achieve interactive framerates even on large datasets. We conduct
in-depth evaluations of the method's performance, including visual quality,
stochastic noise, inference and rendering speeds, and accuracy regarding
illumination and phase function awareness. Results are compared to ray
marching, path tracing and photon mapping. Our findings show that Photon Field
Networks can faithfully represent indirect global illumination across the phase
spectrum while exhibiting less stochastic noise and rendering at a
significantly faster rate than traditional methods.

本篇论文提出了一种名为 Photon Fields Networks 的新方法，能够实现基于体数据的全局实时光照。该方法基于神经网络，通过预先计算的多相位光子缓存进行训练，取得了优异的表现，并能够在大型数据集上实现交互式渲染。

光子场网络用于动态实时体积全局照明

Photon Field Networks for Dynamic Real-Time Volumetric Global  Illumination

Implicit Neural Representations (INRs) have recently exhibited immense
potential in the field of scientific visualization for both data generation and
visualization tasks. However, these representations often consist of large
multi-layer perceptrons (MLPs), necessitating millions of operations for a
single forward pass, consequently hindering interactive visual exploration.
While reducing the size of the MLPs and employing efficient parametric encoding
schemes can alleviate this issue, it compromises generalizability for unseen
parameters, rendering it unsuitable for tasks such as temporal
super-resolution. In this paper, we introduce HyperINR, a novel hypernetwork
architecture capable of directly predicting the weights for a compact INR. By
harnessing an ensemble of multiresolution hash encoding units in unison, the
resulting INR attains state-of-the-art inference performance (up to 100x higher
inference bandwidth) and can support interactive photo-realistic volume
visualization. Additionally, by incorporating knowledge distillation,
exceptional data and visualization generation quality is achieved, making our
method valuable for real-time parameter exploration. We validate the
effectiveness of the HyperINR architecture through a comprehensive ablation
study. We showcase the versatility of HyperINR across three distinct scientific
domains: novel view synthesis, temporal super-resolution of volume data, and
volume rendering with dynamic global shadows. By simultaneously achieving
efficiency and generalizability, HyperINR paves the way for applying INR in a
wider array of scientific visualization applications.

本文引入 HyperINR，一种新颖的超网络架构，通过利用多分辨率哈希编码单元的集合，直接预测一个紧凑的 INR 的权重，从而提高了 INR 的推理性能和支持交互式虚拟现实。

HyperINR: 一种快速预测的超网络，通过知识蒸馏实现隐式神经表示

HyperINR: A Fast and Predictive Hypernetwork for Implicit Neural  Representations via Knowledge Distillation

Since 2016, we have witnessed the tremendous growth of artificial
intelligence+visualization (AI+VIS) research. However, existing survey papers
on AI+VIS focus on visual analytics and information visualization, not
scientific visualization (SciVis). In this paper, we survey related deep
learning (DL) works in SciVis, specifically in the direction of DL4SciVis:
designing DL solutions for solving SciVis problems. To stay focused, we
primarily consider works that handle scalar and vector field data but exclude
mesh data. We classify and discuss these works along six dimensions: domain
setting, research task, learning type, network architecture, loss function, and
evaluation metric. The paper concludes with a discussion of the remaining gaps
to fill along the discussed dimensions and the grand challenges we need to
tackle as a community. This state-of-the-art survey guides SciVis researchers
in gaining an overview of this emerging topic and points out future directions
to grow this research.

本篇论文基于 DL 算法在科学可视化方面的应用，围绕域设置、研究任务、学习类型、网络架构、损失函数和评估指标等六个方面，分类和讨论解决标量场和矢量场数据问题的相关工作，为 SciVis 研究者提供了最新前沿综述和未来增长方向的指引。

DL4SciVis: 科学可视化深度学习的最新调查

DL4SciVis: A State-of-the-Art Survey on Deep Learning for Scientific Visualization

Mayavi is an open-source, general-purpose, 3D scientific visualization
package. It seeks to provide easy and interactive tools for data visualization
that fit with the scientific user's workflow. For this purpose, Mayavi provides
several entry points: a full-blown interactive application; a Python library
with both a MATLAB-like interface focused on easy scripting and a feature-rich
object hierarchy; widgets associated with these objects for assembling in a
domain-specific application, and plugins that work with a general purpose
application-building framework. In this article, we present an overview of the
various features of Mayavi, we then provide insight on the design and
engineering decisions made in implementing Mayavi, and finally discuss a few
novel applications.

本篇文章介绍了开源三维科学可视化工具 Mayavi 的各项特点，重点讨论 Mayavi 的设计和实现，并给出一些新颖应用的例子。