Rapid Labels: Point-Feature Labeling on GPU

Václav Pavlovec, Ladislav Čmolík

View presentation:2021-10-29T15:00:00ZGMT-0600Change your timezone on the schedule page
Exemplar figure, described by caption below
Rapid Labels: Point-Feature Labeling on GPU. Fast greedy method that supports priority-groups of labels and labeling consistent under zoom and pan. All examples were labeled with the proposed method. (left) 3340 airports in US labeled in 72ms. (top right) Line chart labeled without evaluating ambiguity - contains a number of ambiguous labels. (bottom right) Line chart labeled with evaluation of ambiguity - presents a significant improvement in positioning of the labels.
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Direct link to video on YouTube: https://youtu.be/tVEMCigvWkw


Labels, short textual annotations are an important component of data visualizations, illustrations, infographics, and geographical maps. In interactive applications, the labeling method responsible for positioning the labels should not take the resources from the application itself. In other words, the labeling method should provide the result as fast as possible. In this work, we propose a greedy point-feature labeling method running on GPU. In contrast to existing methods that position the labels sequentially, the proposed method positions several labels in parallel. Yet, we guarantee that the positioned labels will not overlap, nor will they overlap important visual features. When the proposed method is searching for the label position of a point-feature, the available label candidates are evaluated with respect to overlaps with important visual features, conflicts with label candidates of other point-features, and their ambiguity. The evaluation of each label candidate is done in constant time independently from the number of point-features, the number of important visual features, and the resolution of the created image. Our measurements indicate that the proposed method is able to position more labels than existing greedy methods that do not evaluate conflicts between the label candidates. At the same time, the proposed method achieves a significant increase in performance. The increase in performance is mainly due to the parallelization and the efficient evaluation of label candidates.