CoordNet: Data Generation and Visualization Generation for Time-Varying Volumes via a Coordinate-Based Neural Network
CoordNet: Data Generation and Visualization Generation for Time-Varying Volumes via a Coordinate-Based Neural Network
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DOI：
10.1109/tvcg.2022.3197203
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发表时间：
2022-08
影响因子：
5.2
通讯作者：
J. Han;Chaoli Wang
中科院分区：
文献类型：
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作者：
J. Han;Chaoli Wang
尽管深度学习已经证明了它在解决各种科学可视化问题方面的能力，但它仍然缺乏跨不同任务的泛化能力。为了应对这一挑战，我们提出了CoordNet，一个单一的基于坐标的框架，处理各种任务相关的随时间变化的体积数据可视化，而无需修改网络架构。我们方法的核心思想是将不同的任务输入和输出分解为统一的表示（即，坐标和值），并学习从坐标到其相应值的函数。我们实现这一目标，使用基于残差块的隐式神经表示架构与周期性激活函数。我们评估CoordNet的数据生成（即，时间超分辨率和空间超分辨率）和可视化生成（即，视图合成和环境遮挡预测）任务。实验结果表明，CoordNet实现了更好的定量和定性结果比国家的最先进的方法在所有的评估任务。源代码和预训练模型可在https://github.com/stevenhan1991/CoordNet上获得。
Although deep learning has demonstrated its capability in solving diverse scientific visualization problems, it still lacks generalization power across different tasks. To address this challenge, we propose CoordNet, a single coordinate-based framework that tackles various tasks relevant to time-varying volumetric data visualization without modifying the network architecture. The core idea of our approach is to decompose diverse task inputs and outputs into a unified representation (i.e., coordinates and values) and learn a function from coordinates to their corresponding values. We achieve this goal using a residual block-based implicit neural representation architecture with periodic activation functions. We evaluate CoordNet on data generation (i.e., temporal super-resolution and spatial super-resolution) and visualization generation (i.e., view synthesis and ambient occlusion prediction) tasks using time-varying volumetric data sets of various characteristics. The experimental results indicate that CoordNet achieves better quantitative and qualitative results than the state-of-the-art approaches across all the evaluated tasks. Source code and pre-trained models are available at https://github.com/stevenhan1991/CoordNet.