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1Web3D 2006
Using Expressive Rendering for Remote Visualization of Large City Models
Wednesday, April 19
Jean-Charles QuilletGwenola ThomasXavier GranierPascal GuittonJean-Eudes Marvies
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Introduction
Remote visualization is concerned with improving realistic rendering• Complex 3D models
• Complex lighting system
• Progressive texture transmission
For trip or assisted navigation• The solutions mostly rely on 2D information
Available on PDA and mobile devices combined with GPS• Powerful navigation tool
Remote visualization city models on mobile devices
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Introduction
Remote visualization context• The server disposes of the entire geometry of the virtual city
• It sends a subset of the city model to the client on demand
Virtual cities• Simplified geometry
• Textured by photographs of real building facades
Issues• Limited network bandwidth
• Not adapted to the small size of the display
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Our approach
Expressive or non-photorealistic rendering (NPR) with feature lines
• Making more legible image
• More rapid transmission for data
We present a pipeline able
• To extract the feature lines from photographs of building facades
• To transmit and render them on clients
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Previous work
NPR for remote rendering• [Hekmatza et al. 2002] and
[Diepstraten et al. 2004]
• Lines are computed for each new viewpoint
• Adapted to smooth 3D models
For cities, feature lines are related to the façades
3D model
Server ClientViewpoint
Extracted lines
2D Line-based rendering
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System overview
Buildings extrusion
Extraction of feature lines
Spatial subdivision
Visibility computation
Model streaming
Line rendering
City footprints+ Textures
NPRdatabase
Optimizeddatabase
NPRmodeling
Databaseoptimization
Client-servervisualization
Offl
ine
Onl
ine
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Scene subdivision
Goal: produce a 3D database ready to be streamed using visibility information
• VRML97 extension for visibility streaming [Marvie et al. 2004]
Scene subdivision in cells to produce the navigation space
• Constrained BSP subdivision [Marvie PhD 2003]
Computation of Potentially Visible Object Set (PVOS) for each cell• Compute cell-to-objects visibility relationships
• OpenGL shooting algorithm [Marvie PhD 2003]
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Visibility streaming
The process is initiated by the client
• Buffering step:
– the starting cell is downloaded in a synchronous way together with its PVOS
• During navigation:
– Prediction is made about the next visited cell
– This cell and its adjacent cells are downloaded together with their PVOS in an asynchronous way
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Feature extraction of the facades
Edge detection Vectorization Rendering
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Edge detection
Classical approach• Gradient approximation using a convolution kernel: Sobel [Pingle 1969],
Roberts [1965], Prewitt [1970]
• Simple thresholding
• The contours are not 1 pixel wide, needed for vectorization
Canny edge detector• Gaussian blur
• Gradient computation
• Non-maximal suppression: insures 1 pixel wide contours
• Hysteresis thresholding
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Pixel chaining
1. Contours
2. Junction and end-points detection
3. Graph initialization
4. Graph filling
5. Loop processing
6. Vectorization
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Vectorization
Vectorization of the pixel chains• Minimization of the distance between the approximation and the curve
[D.H. Douglas 1973], [de Figueiredo 1995]
• Best approximation of the angular [Dunham 1986] or the curvature [Asada and Brady 1986]
• Minimization of the area between the approximation and the curve [Wall and Danielsson 1984]
Post processing and cleaning: simple heuristics• Junction correction
• Noise removing
• Line straightening
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Rendering of lines
Lines are transmitted as IndexedLineSet
Lines are rendered with a constant width of one pixel
At some point the screen is saturated with lines
• Need to remove some lines
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Rendering of lines
Level of details• Depending on the distance the
façade is viewed
• We draw a subset of the lines
Advantages• Reducing the line density on
screen
• Less geometric primitives are drawn, rendering time is improved
Line selection criterion• Based on line length
• Same lines are used between each level
• Visual continuity between the different levels
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Results
We compare our approach to textured rendering• On data size
• Rendering speed
Hardware: DELL Axim X50V• 624 MHz ARM processor
• 64 MB of RAM memory
• Intel 2700 GPU
Software based on OpenGL-ES standard• Magellan framework for WinMobile (VRML/X3D based streaming & rendering)
• Intel SDK for hardware acceleration
• Software-based library developed by Hybrid Graphics
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Results
Initial data set
• Fixed viewpoint
– Low resolution pictures (250x320): French yellow pages website
– High resolution pictures (3008x2000): digital camera
• City walkthrough
– Model and textures maps of Bordeaux [Hachet Guitton 2001]
– Small texture map sizes (~64x64)
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Fixed viewpoint
Data size
• Vector data smaller than original pictures
• Drawn images smaller than original pictures
60.56 KB37.55 KB387.76 KBHigh resolution
3.13 KB4.16 KB8.55 KBLow resolution
Drawn images PNGVector dataJPEG
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Fixed viewpoint
Rendering speed
• Using low resolution images– On software-based rendering
– texture ~ lines + LOD: 6 fps– Using hardware acceleration
– Texture: 39.6 fps – Lines + LOD: 17.3 fps (because no display lists on GL|ES)
• Using high resolution images– Still practicable using lines + LOD: 3 fps– Results for lines are better using software-based rendering
– no transfer on the bus for SW based
• Significant better performances using LOD than full line rendering
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Moving viewpoint: the complete city model
Record statistics during a city walkthrough• Memory used
• Frame rate
• Rendering time
• Scene graph traversal
• Download bitrate
Using different models• Geometry only as reference
• Textured based model
• Model together with its full line appearance description
• Model that takes advantage of the presented LOD technique
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Moving viewpoint: the complete city model
Average size of downloaded files
• Textures: 35.61 KB
• Lines: 7.55 KB
Memory occupation 2 MB smaller for the lines than for textures
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Moving viewpoint: the complete city model
Rendering speed (SW vs HW)
• Textures: 2.18 fps / 11.97 fps• Lines: 1.17 fps / 1.25 fps• Lines + LOD: 2.28 fps / 5.45 fps
In software
• Lines + LOD ~ textures (because of their small resolution)
In hardware
• Texture gets better result (because of bus transfers)
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Moving viewpoint: the complete city model
The rendering is divided in two steps• Scene graph traversal time• Rendering time
Scene graph traversal time• Texture: 12 ms• Lines + LOD: 61 ms• The scene graph is simpler for the texture-based rendering
Rendering time• With hardware acceleration:
– Texture: 90 ms – Lines + LOD: 258 ms
• With software library:– Texture: 388 ms– Lines + LOD: 347 ms
Improve the scene graph traversal time by implementing a dedicated node
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Moving viewpoint: the complete city model
Bitrate measurement: the rate the data are sent by the server to the moment they are available in the client scene graph.1. Data transmission over the network
2. Data decompression
3. VRML97 parsing and conversion to fixed point
4. Node initialization
Resulting bitrate• for the lines: 17306 bps
• for the textures: 26902 bps
• Lines bitrate lower than map’ ones due to conversion to fixed point
• Might need fixed point fields types in VRML/X3D
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Conclusion
Our approach• Based on expressive rendering
• Features lines are extracted from original texture
• The resulting data set is smaller than the original one
Experiments• On PDA
• The result conveys the required information
• The amount of data to transmit is greatly reduced
• We can obtain interactive rendering even using software 3D rendering
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Future work
Feature lines extraction
• Improve its robustness and efficiency
• Perform a cognitive study on scene recognition
Rendering system
• Use up-coming vector processor based on OpenVG standard
• Allow integration of larger range of NPR styles
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Thank you
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