Download to read offline
Uploaded byChristian Kehl
Efficient Navigation in Temporal, Multi-Dimensional Point Sets (April 2013)
1. The document discusses algorithms and techniques for efficient navigation of temporal, multi-dimensional point set data. 2. A goal is to develop visualization algorithms that support user navigation through time-series data, including real-time rendering, user-centered browsing, and navigation via visual summaries. 3. The research will focus on scalable rendering and visualization of time-dependent point sets, efficient browsing of time-dependent datasets, and navigation using visual summaries to guide users through important events.
More Related Content
Similar to Efficient Navigation in Temporal, Multi-Dimensional Point Sets (April 2013)
Efficient Navigation in Temporal, Multi-Dimensional Point Sets (April 2013)
- 1. 1Challenge the future EfficientNavigation in Temporal, Multi-Dimensional Point Sets Efficiёnte navigatie in tijd-afhankelijke, multi-dimensionale puntengegevens Christian Kehl
- 2. 2Challenge the future GeneralIntroduction • Plenty of point set scans available • Many Application Areas • Home Entertainment • Construction Management • Disaster Management
- 3. 3Challenge the future GeneralIntroduction • Problem 4D data: technical limitations of rendering system • user‘s interests hard to find by traversing time-series data sets • Goal: Visualisation algorithms for supportive user navigation • Approaches: • real-time rendered data traversal • user-centred browsing • navigation via visual summaries Rendering & Navigation Delfland dataset 1 time step 12.5 km * 10 km takes up to 3 hours to inspect in detail User Interest: (top) Landslide probability; (right) Door Surveillance
- 4. 4Challenge the future ResearchStatement “I will search for algorithms and scalable representations that allow for interaction, queries, and exploration of time-dependent point data .”
- 5. 5Challenge the future Subtopics 1.Scalable Rendering and Visualisation of time-dependent Point Sets 2. Efficiently Browsing through time-dependent Datasets 3. Navigation by Visual Summaries
- 6. 6Challenge the future ScalableRendering and Visualisation of time-dependent Point Sets • Interactive Rendering of large point sets already demanding • additional, time-related challenges: • just developing branch, virtually no data sets openly available => no available rendering approaches • Rendering of multiple time steps faces technical challenges 3Di project: currently more than 12TB and growing
- 7. 7Challenge the future ScalableRendering and Visualisation of time-dependent Point Sets • Goal: efficient rendering system • displays massive point sets • multiple time steps at the same time • exploits visual and technical limitations • Contribution: • time-dependent LoD technique by continuous refinement • temporal caching of point sets
- 8. 8Challenge the future Subtopics 1.Scalable Rendering and Visualisation of time-dependent Point Sets 2. Efficiently Browsing through time-dependent Datasets 3. Navigation by Visual Summaries
- 9. 9Challenge the future Efficientlybrowsing through time- dependent datasets • Use Case: Surveillance of restricted areas -> user-defined monitoring of areas • Use Case: Natural Disaster Monitoring -> changing demands and interests, depending on zoom level
- 10. 10Challenge the future Efficientlybrowsing through time-dependent datasets Problems: • browse while only showing user-specific interest • remove/hide redundant data • selection and interactive exploration of temporal data • handling varying user interests according to user viewpoint
- 11. 11Challenge the future Efficientlybrowsing through time- dependent datasets Goal: Exploring navigation techniques • user-centred interaction • visual querying • Level-of-Abstraction
- 12. 12Challenge the future Subtopics 1.Scalable Rendering and Visualisation of time-dependent Point Sets 2. Efficiently Browsing through time-dependent Datasets 3. Navigation by Visual Summaries
- 13. 13Challenge the future Navigationby Visual Summaries • “Visual Summary”: visual and effective way to summarize complex datasets • various applications in entertainment, construction etc. Creation
- 14. 14Challenge the future Navigationby Visual Summaries • party in Kinect-supervised house • lots of objects (people) -> lots of events and interests • goal: summarize the party to re-live it another day Creation - Entertainment
- 15. 15Challenge the future Navigationby Visual Summaries • construction of building demands experts of different fields • time constraints prevent meetings at construction site • goal: summarize recent construction events for remote planning Creation – Construction
- 16. 16Challenge the future Navigationby Visual Summaries Problems: • suitable representations • spatio-temporal incoherence • events wide-spread in space and time across the dataset • suitable guidance to important events in the dataset
- 17. 18Challenge the future Navigationby Visual Summaries • user-driven interconnection to group objects of different steps • intuitive user interface to regulate amount of spatial- and temporal coherence • test visual representations to determine the most suitable one • compound visual summary as an album of impressions Approach - Entertainment example
- 18. 19Challenge the future Navigationby Visual Summaries • focus on Guidance along events • visual 4D tour • 4D scene capture as interactive representation of a summary Approach – Construction Example
- 19. 20Challenge the future Conclusion •focus: Visually navigating efficiently in time-dependent, multi- dimensional, scanned data • applications: • Efficient Visual Surveillance • Disaster Assessment with dynamically changing user interests • Home Entertainment: Re-live a 3D-recorded party another day • Visually guide construction processes
- 20. 21Challenge the future Conclusion •Necessary techniques: • Real-time Rendering of Datasets • Efficient Browsing through Datasets according to user interests • Visual Summaries and their use as Guidance Method in Datasets
- 21. 22Challenge the future Thefollowing 3 months • finish paper “Interactive Rendering of Large-Scale, Geospatial Data“ (PROM-4 Scientific Writing) • generate artificial, temporal test dataset Time-dependent Level-of-Detail technique by continuous refinement • Construct tree structure via spatial subdivision • Tree node refers to a list of time steps • Each time-step stores hierarchical LoD tree • low-resolution height maps or volumes per cell • On traversal – continuous refinement: • morph previously loaded points by rough estimate • replace them gradually by newly loaded points Height Difference f(t-1, t)