AI 中文总结
该研究针对二维标量场轮廓集合可视化，提出基于概率潜在空间建模的方法，在潜在空间用概率表示建模成员，支持相关计算与聚类，能估计潜在概率分布创建密度图，经比较和示例验证了方法的有效性。
AI 中文摘要
我们通过概率建模提出了一种用于轮廓集合的新可视化方法。旨在提高不同视觉表示之间的连贯性，比如二维标量场集合的轮廓箱线图和密度图。我们在潜在空间中用概率表示对每个集合成员进行建模，即变分自编码器（VAE）的空间数据特征的低维表示。此后，基于成员的成对相似性测量矩阵支持高效的数据深度计算和不确定性感知聚类。我们利用VAE的能力估计潜在概率分布以创建比现有方法更连贯地与成员分布对齐的密度图。通过与现有技术的数值比较以及合成和真实世界集合数据集的可视化示例评估了我们方法的有效性。
英文摘要
We present a new visualization method for contour ensembles through probabilistic modeling. We aim to improve the coherence between different visual representations, such as contour boxplots and density plots for a 2D scalar field ensemble. We model each ensemble member with a probabilistic representation in the latent space, i.e., a lower-dimensional representation of spatial data features, of a variational autoencoder (VAE). Thereafter, efficient data depth computation and uncertainty-aware clustering are supported based on a matrix of pair-wise similarity measurements of members. We estimate the underlying probability distribution by leveraging the power of VAE to create density plots that align more coherently with member distributions than existing methods. The effectiveness of our method is evaluated through numerical comparisons with existing techniques, and visualization examples of synthetic and real-world ensemble datasets.
CommentsAccepted by IEEE VIS 2026. To appear in IEEE Transactions on Visualization and Computer Graphics
