当前位置: X-MOL 学术IEEE Trans. Vis. Comput. Graph. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
FlowNet: A Deep Learning Framework for Clustering and Selection of Streamlines and Stream Surfaces.
IEEE Transactions on Visualization and Computer Graphics ( IF 4.7 ) Pub Date : 2018-11-13 , DOI: 10.1109/tvcg.2018.2880207
Jun Han , Jun Tao , Chaoli Wang

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.

中文翻译:

FlowNet:一种用于对流线和流表面进行聚类和选择的深度学习框架。

为了有效地可视化流动,识别代表性的流动线或表面是已研究的重要问题。但是,对于线和曲面,都无法解决任何问题。在本文中,我们介绍了FlowNet,这是一个用于对流线和流表面进行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体积,然后使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,以进行错误估计和网络训练。一旦收敛,特征描述符就可以很好地表示潜在空间中的流线或表面。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。这导致了一个可视界面,用于通过聚类,过滤和代表选择来探索流线或曲面的集合。提供直观的用户交互,以轻松地进行集合的视觉推理。我们从多个角度验证和解释了我们的深度学习框架,使用多个具有不同特征的流场数据集演示了FlowNet的有效性,并将我们的方法与最新的流线和流表面选择算法进行了比较。提供直观的用户交互,以轻松地进行集合的视觉推理。我们从多个角度验证和解释了我们的深度学习框架,使用多个具有不同特征的流场数据集证明了FlowNet的有效性,并将我们的方法与最新的流线和流表面选择算法进行了比较。提供直观的用户交互,以轻松地进行集合的视觉推理。我们从多个角度验证和解释了我们的深度学习框架,使用多个具有不同特征的流场数据集演示了FlowNet的有效性,并将我们的方法与最新的流线和流表面选择算法进行了比较。
更新日期:2020-02-28
down
wechat
bug