FlowNet: A Deep Learning Framework for Clustering and Selection of Streamlines and Stream Surfaces
FlowNet: A Deep Learning Framework for Clustering and Selection of Streamlines and Stream Surfaces
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DOI：
10.1109/tvcg.2018.2880207
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发表时间：
2020-04-01
影响因子：
5.2
通讯作者：
Wang, Chaoli
中科院分区：
文献类型：
--
作者：
Han, Jun;Tao, Jun;Wang, Chaoli
为了有效地进行流动显示，识别代表性的流线或流面是一个重要的问题，已经被研究。然而，没有工作可以同时解决线和面的问题。在本文中，我们介绍了FlowNet，这是一个用于流线和流表面聚类和选择的单一深度学习框架。给定从流场数据集生成的流线或流表面的集合，我们的方法将它们转换为二进制卷，然后采用自动编码器来学习它们各自的潜在特征描述符。这些描述符用于重建二进制卷，以进行误差估计和网络训练。一旦收敛，特征描述符可以很好地表示潜在空间中的流线或曲面。我们对这些特征描述符进行降维，并相应地对投影结果进行聚类。这导致了一个可视化界面，用于通过聚类、过滤和选择代表来探索流线或曲面的集合。提供直观的用户交互，以便轻松地对集合进行视觉推理。我们从多个角度验证和解释我们的深度学习框架，使用不同特征的几个流场数据集展示FlowNet的有效性，并将我们的方法与最先进的流线和流面选择算法进行比较。
For effective flow visualization, identifying representative flow lines or surfaces is an important problem which has been studied. However, no work can solve the problem for both lines and surfaces. In this paper, we present FlowNet, a single deep learning framework for clustering and selection of streamlines and stream surfaces. Given a collection of streamlines or stream surfaces generated from a flow field data set, our approach converts them into binary volumes and then employs an autoencoder to learn their respective latent feature descriptors. These descriptors are used to reconstruct binary volumes for error estimation and network training. Once converged, the feature descriptors can well represent flow lines or surfaces in the latent space. We perform dimensionality reduction of these feature descriptors and cluster the projection results accordingly. This leads to a visual interface for exploring the collection of flow lines or surfaces via clustering, filtering, and selection of representatives. Intuitive user interactions are provided for visual reasoning of the collection with ease. We validate and explain our deep learning framework from multiple perspectives, demonstrate the effectiveness of FlowNet using several flow field data sets of different characteristics, and compare our approach against state-of-the-art streamline and stream surface selection algorithms.