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Visual clutter management in augmented reality: Effects of three label separation methods on spatial judgments
This paper reports an experiment comparing three label separation methods for reducing visual clutter in Augmented Reality (AR) displays. We contrasted two common methods of avoiding visual overlap by moving labels in the 2D view plane with a third that distributes overlapping labels in stereoscopic...
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Main Authors: | , , , |
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Format: | Conference Proceeding |
Language: | English |
Subjects: | |
Citations: | Items that cite this one |
Online Access: | Request full text |
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Summary: | This paper reports an experiment comparing three label separation methods for reducing visual clutter in Augmented Reality (AR) displays. We contrasted two common methods of avoiding visual overlap by moving labels in the 2D view plane with a third that distributes overlapping labels in stereoscopic depth. The experiment measured user identification performance during spatial judgment tasks in static scenes. The three methods were compared with a control condition in which no label separation method was employed. The results showed significant performance improvements, generally 15-30%, for all three methods over the control; however, these methods were statistically indistinguishable from each other. Indepth analysis showed significant performance degradation when the 2D view plane methods produced potentially confusing spatial correlations between labels and the markers they designate. Stereoscopically separated labels were subjectively judged harder to read than view-plane separated labels. Since measured performance was affected both by label legibility and spatial correlation of labels and their designated objects, it is likely that the improved spatial correlation of stereoscopically separated labels and their designated objects has compensated for poorer stereoscopic text legibility. Future testing with dynamic scenes is expected to more clearly distinguish the three label separation techniques. |
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DOI: | 10.1109/3DUI.2009.4811215 |