A simple method for computing general position in displaying three-dimensional objects
Computer Vision, Graphics, and Image Processing
A vector space model for automatic indexing
Communications of the ACM
Viewpoint Selection using Viewpoint Entropy
VMV '01 Proceedings of the Vision Modeling and Visualization Conference 2001
Importance-Driven Focus of Attention
IEEE Transactions on Visualization and Computer Graphics
Viewpoint selection for intervention planning
EUROVIS'07 Proceedings of the 9th Joint Eurographics / IEEE VGTC conference on Visualization
A dual-mode user interface for accessing 3D content on the world wide web
Proceedings of the 21st international conference on World Wide Web
Proceedings of the 18th International Conference on 3D Web Technology
On the design of a Dual-Mode User Interface for accessing 3D content on the World Wide Web
International Journal of Human-Computer Studies
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This paper presents the concept and an evaluation of a novel approach to support students to understand complex spatial relations and to learn unknown terms of a domain-specific terminology with coordinated textual descriptions and illustrations. Our approach transforms user interactions into queries to an information retrieval system. By selecting text segments or by adjusting the view to interesting domain objects, learners can request additional contextual information. Therefore, the system uses pre-computed multi-level representations of the content of explanatory text and of views on 3D models to suggest textual descriptions or views on 3D objects that might support the current learning task.Our experimental application is evaluated by a user study that analyzes (i) similarity measures that are used by the information retrieval system to coordinate the content of descriptive texts and computer-generated illustrations and (ii) the impact of the individual components of these measures. Our study revealed that the retrieved results match the preferences of the users. Furthermore, the statistical analysis suggests a rough value to cut-off retrieval results according to their relevancy.