Serving and rendering huge point clouds on web and mobile devices.
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Transcript of Serving and rendering huge point clouds on web and mobile devices.
Glob3 Mobile: Serving and rendering huge point clouds on mobile devices and web
pages(aka: Feature Streaming server & Client)
Manuel de la CalleFOSS4G 2015@mdelacalle
Glob3 Mobile (G3M) is: an open source* API to build native maps applications that runs on any device
(*) github.com/glob3mobile/g3m
Multiplatformnative performance everywhere
2D/3D Maps
Any kind of data
Offline / OnlineCamera and Models animations
Utilities
Streaming
Rendering and interaction with huge datasetsSmooth experienceLooking for the best Performance● Optimize network usage, download only the data
relevant to the current visible area of the map● Minimize the waiting time to view/interact
○ download the “most significative” features first○ show the incoming data as soon as it’s available○ when more detailed data arrives, update the view
with more data
Why streaming geo features?
Architecture bird viewData importation
LOD Preprocessing
Rendering
Data importation
- Import huge unsorted data into a Quadtree on disk- No size limit (ok, not really, the disk space is the limit)- The resulting Quadtree gives the first categorization of the data into “Tiles”- Produces useful metadata like Bounding Box, Features Count, Density, etc
LOD (Level of Detail) Preprocessing
- produces the intermediate LOD levels
- sort the data in a “stream” friendly format- most-significative features go first, least-significative go last
- LOD Strategies- Shape preserving → for LiDAR point-clouds- Sorting → for point-vector datasets with a clear sorting criteria- Clustering → for point-vector datasets with all the features are “equals”
LOD Strategies
Shape preserving- Selection of points where the most-significative are the ones that describe
the general shape of the point-cloud. The shape of the cloud is always preserved (inclusive in the less-resolution levels).
Sorting- The sorting criteria defines which features are the most and least
significative.Clustering
- The intermediate levels are filled with Clustering information that describes the structure of the full-detailed-levels
LOD 0
LOD 1
LOD 2
Rendering
- download of metadata- size, covered sector, min/max height, etc- description of Quadtree nodes
- bounding box- average point- LOD Levels
- based on the projected size of the BB, estimate how many LOD levels are needed
- download the next to the current loaded level- cancel current download in case it's not more needed
Streaming 7
Demo time!
http://www.mapboo.comhttp://point-cloud.glob3mobile.comGoogle playApple Store
Glob3 Mobile: Serving and rendering huge point clouds on mobile devices and web
pages(aka: Feature Streaming server & Client)
Manuel de la CalleFOSS4G 2015@mdelacalle