Schreck,T., Schuessler,M., Zeilfelder,F., Worm,K.
Visualization of 2D point clouds is one of the most basic yet one of the most important problems in many visual data analysis tasks. Point clouds arise in many contexts including scatter plot analysis, or the visualization of high-dimensional or geo-spatial data. Typical analysis tasks in point cloud data include assessing the overall structure and distribution of the data, assessing spatial relationships between data elements, and identification of clusters and outliers. Standard point-based visualization methods do not scale well with respect to the data set size. I.e., as the number of data points and data classes increases, the display quickly gets crowded, making it difficult to effectively analyze the point clouds We propose to abstract large sets of point clouds to compact shapes, facilitating the scalability of point cloud visualization w.r.t. data set size. We introduce a novel algorithm for constructing compact shapes that enclose all members of a given point cloud, providing good perceptional properties and supporting visual analysis of large data sets of many overlapping point clouds. We apply the algorithm in two different applications, demonstrating the effectiveness of the technique for large point cloud data. We also present an evaluation of key shape metrics, showing the efficiency of the solution as compared to standard approaches.