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Butterfly Plots for Visual Analysis of Large Point Cloud Data

Schreck, Tobias and Schüßler, Michael and Worm, Katja and Zeilfelder, Frank (2008):
Butterfly Plots for Visual Analysis of Large Point Cloud Data.
pp. 33-40, University of West Bohemia, Plzen, WSCG 2008. Full Papers Proceedings, [Conference or Workshop Item]

Abstract

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. Specifically, 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 with respect to 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.

Item Type: Conference or Workshop Item
Erschienen: 2008
Creators: Schreck, Tobias and Schüßler, Michael and Worm, Katja and Zeilfelder, Frank
Title: Butterfly Plots for Visual Analysis of Large Point Cloud Data
Language: English
Abstract:

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. Specifically, 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 with respect to 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.

Publisher: University of West Bohemia, Plzen
Uncontrolled Keywords: Forschungsgruppe Visual Search and Analysis (VISA), Visual analytics, Point clouds, Shape construction
Divisions: UNSPECIFIED
20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Event Title: WSCG 2008. Full Papers Proceedings
Date Deposited: 16 Apr 2018 09:03
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