research & innovation 1 an industry perspective on vvg research oliver grau bbc research &...
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1 Research & Innovation
An Industry Perspective on VVG Research
Oliver Grau
BBC Research & Innovation
VVG SUMMER SCHOOL '07
2 Research & Innovation
Overview
• Introduction– (Computer) Vision, Video & Graphics in media production
• Integration of (real) Video & Graphics• Production visualisation tools• Free-viewpoint video for Sport visualisation
• Summary
3 Research & Innovation
Introduction
• BBC is a public funded broadcaster producing different kinds of media. Mainly: Radio, TV and Online
• Video: One of the ‘Main businesses’• Graphics – used to:
– Add editorial value– Make programmes (visually) more exciting– Create new user’s experience
• Vision: Big toolbox to make things happen
4 Research & Innovation
Introduction
• Added editorial value
5 Research & Innovation
Introduction
• New user experience: 3D Virtual environment
CBBC Adventure Rock: www.bbc.co.uk/cbbc/adventurerock/
6 Research & Innovation
Introduction
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Integration of video & graphics
• ‘Classical’ application: Weather forecast
• Studio with chroma-key facility
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What makes an integrated ‘virtual scene’ looking ‘real’
Real Scene Virtual Scene
Depth perception
Camera perspective
Occlusions
Shadows
Reflections
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Integration of video & graphics
• Virtual studio – inserting actor into virtual scene
Virtual background
Moving camera
Camera Tracking
10 Research & Innovation
3D reconstruction of dynamic scenes in the studio
• Virtual studios have limited features regarding optical interaction of real and virtual objects
• More realistic integration requires full optical interaction in 3D– Shadows + reflections– Occlusions– Free choice of camera angle
11 Research & Innovation
3D reconstruction of dynamic scenes in the studio
• 12 fixed, calibrated cameras with chroma-keying facility• Developed in the IST-ORIGAMI project
12 Research & Innovation
Overview
Block diagram
Volumetric visual hull
computation..
Input images,Camera parameters
Surface computationMarching cube
algorithm
Volumetric model Surface model
Texture mapping
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Visual hull computation
• Visual hull computation from image silhouettes
Camera-1
Camera-2
Camera-3
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Visual hull computation
• Visual hull computation from image silhouettes
Camera-1
Camera-2
Camera-3
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Artefacts in Visual hull-based 3D reconstruction
• Fundamental feature: – only parts of the volume that are visible as background in at
least one of the silhouette images are taken out no concavities can be modelled
• Occlusion errors Phantom volumes Reduced by increasing number of cameras
• Approximation errors
• Sampling errors
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Visual hull-based 3D reconstruction
• Sampling errors– Binary volumetric scene representation Quantisation noise
12 cameras6 cameras12 cameras
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Artefacts in dynamic visual hulls
• “Moving edges” as effect of discrete voxel grid
C2
C1
object
reconstruction
(moving) edge
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Improved visual hull computation: Super-sampling
– Sub-divide voxels by LSS levels
– Assign voxel a (pseudo-) continuous value
Camera-1
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Improved visual hull computation
• Gaussian smoothing• (Optional) complexity reduction
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Results 3D model using super-sampling (20000 triangles)
After Gaussian smoothing
Standard visual hull / marching cube
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Results
• Video from ORIGAMI demo production
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Results
• Non-photorealistic rendering
23 Research & Innovation
Re-lighting
Studio illumination map
3D scene model
irradiance
Specular component
Original image
Light-neutral image
24 Research & Innovation
Re-lighting