AI 中文总结
研究大规模线集合可视化中轨迹连续性与视觉可扩展性的矛盾，提出用路径积分轨迹保真度度量补充密度视图，通过构建结构不一致场定位相关区域，经动态校正等实现交互式分析，能消除密集线模式歧义。
AI 中文摘要
在可视化大规模线集合时，轨迹连续性和视觉可扩展性本质上是相互对立的。以轨迹为中心的渲染保留路径信息，但随着线密度增加和相互遮挡占主导，会迅速退化为杂乱。基于场的密度表示增强了可见性，但牺牲了结构连贯性。我们用路径积分轨迹保真度度量来补充基于密度的视图，该度量量化每个轨迹与周围张量场的一致性。通过将这种以通道为中心的结构支持投影回图像空间，得到结构不一致场，它能定位密集模式对应连贯结构与不一致、异常值或连通性引起的模糊性的区域。动态留一法校正减少路径积分中的自偏差。高效的固定网格更新与前缀和评估相结合，实现交互式分析和连贯结构的迭代提取。合成基准、可扩展性分析和实际案例研究表明，与传统密度视图结合时，该方法通过揭示仅密度表示中隐藏的空间局部连贯性和结构崩溃，消除密集线模式的歧义。
英文摘要
When visualizing large-scale line ensembles, trajectory continuity and visual scalability are inherently antagonistic. Trajectory-centric renderings preserve path information but rapidly degenerate into clutter as line density increases and mutual occlusion dominates. In contrast, field-based density representations enhance visibility while sacrificing structural coherence: density reflects accumulation rather than agreement, such that scalar aggregation alone cannot discriminate between consistent and conflicting configurations. Rather than replacing density-based views, we complement them with a path-integrated trajectory-fidelity measure that quantifies the agreement of each trajectory with a surrounding tensor field. By projecting this passage-centered structural support back into image space, we obtain what we call a Structural Inconsistency Field, which localizes regions where dense patterns correspond to coherent structure versus disagreement, outliers, or connectivity-induced ambiguity. Dynamic leave-one-out correction reduces self-bias in the path integral. Efficient fixed-grid updates combined with prefix-sum evaluation enable interactive analysis and iterative extraction of coherent structures. Synthetic benchmarks, scalability analyses, and real-world case studies demonstrate that, when paired with conventional density views, the proposed method disambiguates dense line patterns by exposing spatially localized coherence and structural breakdown that remain concealed in density-only representations.
Comments20 pages, 24 figures. Accepted at IEEE VIS 2026
