2D & 3D VIDEO PROCESSING FOR
IMMERSIVE APPLICATIONS
Emerging Convergence of Video, Vision & Graphics
Harpreet S. Sawhney
Rakesh Kumar
ACKNOWLEDGEMENTS
Collaborative Work with:
Hai Tao
Yanlin Guo
Steve Hsu
Supun Samarasekera
Keith Hanna
Aydin Arpa
Rick Wildes
TECHNICAL SUCCESS OF CONVERGENCE
TECHNOLOGIES
PC based near real-time mosaicing
Image based modeling for Entertainment
Automated Video Enhancement: VHS-to-DVD
Real-time Video Insertion
Iris recognition, active vision
Immersive and Interactive Telepresence
Modes of Operation
Observation Mode
Conversation Mode
Interaction Mode
User observes a remote site
from any perspective.
Users talk and observe one
another as if in the same room.
Remote users share a common
work space.
User “walks” through site to
view activities of interest
“up close”.
Users walk around yet maintain
eye contact.
Users observe each other’s hands
as they manipulate shared objects,
such as war room wall displays.
Example: security, facility guards,
sports & entertainment
Example: immersive teleconferencing
Example: mission planning,
remote surgery
Quality of Service for Tele-presence
Critical Issues
• High quality for immersive experience
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Artifact free recovery of 3D shape from video streams
Efficient 3D video representation and compression
High quality rendering of new views using 3D shape and video streams
Bandwidth available in the Next Generation Internet
• Low latency for interactive applications
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–
–
–
Real time 3D geometry recovery at the content server end
Real time new view rendering at the browser client end
Adaptive Stream management to handle user requests and network loads
Error resilience and concealment to fill in missing packets
Convergence Technologies
… for immersive & interactive visual applications ...
• Vision algorithms: High-quality 3D shape recovery
and dynamic scene analysis
• ASICs, high performance hardware: Real-time video processing
• Compact, low-cost cameras: CMOS cameras
• Low latency and high quality compression: Error resilience
• Real time view synthesis : Standard platforms, e.g. PCs
• Immersive Displays
Vision algorithm performance over time
High Quality 3d shape extraction
Immersive
Telepresence
Algorithm Complexity
2000
Video registration to 3D site models
1998
Coarse 3D Depth Recovery
1995
2D Video Insertion
Georegistration
visual
databases
Face Finding for
Iris Recognition
Real-time insertion in
Live TV
1993
2D Stabilization
1990
Mosaicing for entertainment &
surveillance
Time
HW Performance/Size/Cost over time
VFE-100
1992
VFE-200
1997
ACADIA ASIC
2000
• Sarnoff ACADIA ASIC performance
• 100 MHz system clock, processes 100 million pixels/sec in each processing
element
• 10 billion operations / sec total IC performance
• 800 MB/sec SDRAM interface using 64-bit bus
• Enables building smart 3D cameras for immersive applications.
Application Performance
• Parametric Motion : Stabilization & Mosaicing
– 720x240 fields @ 60 Hz OR 720x480 frames @ 30 Hz
• Pyramid based Fusion : Dynamic Range, Focus
Enhancement
– 720x240 fields @ 60 Hz OR 720x480 frames @ 30 Hz
• Stereo Depth Extraction
– 720x240 field 32 disparity levels in 4 ms (250 Hz)
– 720x240 field 60 disparity levels in 10 ms (100 Hz)
– 60 disparities on 1k x 1k images at 55 ms (18 Hz)
Sarnoff Compression Technology
… Required algorithm components for tele-presence are emerging ...
Algorithm Complexity
MPEG4, Progressive Encoding E-vue
1999
Low Latency MPEG2 multiplexing service
1998-1999
Just Noticeable Difference (JND):
MPEG2 Encoding and Quality
Tektronix
Measurement
1997-1998
VideoPhone: H.263
1997-1998
LG Electronics
MPEG2: Encoding and Transmission DIREC-TV & HDTV
1993- 1996
Pyramid & Wavelet based Encoding Still Image Compression
1988-1993
Time
ICTV
A FRAMEWORK FOR VIDEO PROCESSING
ALIGN
2D & 3D MODELS OF MOTION & STRUCTURE
MODEL-BASED IMAGE SEQUENCE ALIGNMENT
TEST
WARP/RENDER WITH 2D/3D MODELS
TEST ALIGNMENT QUALITY
SYNTHESIZE
CREATE OUTPUT REPRESENTATIONS
Highlights of Sarnoff’s Video Analysis Technologies
… framework applied to a create immersive representations ...
2D Immersive
& Layered Representations
Spherical Mosaics
Dynamic & Synopsis Mosaics
Core Vision Algorithms
for (Real-time)
Motion & 3D Video Analysis
Stereo & Video Sequence
Enhancement
Hi-Q IBR based mixed resolution synthesis
Video Quality Enhancement for efficient compression
Model-centric
Video Visualization
Dynamic model & video
visualization
Geo-registration with reference
image database
Multi-camera Immersive
Dynamic Rendering
Hi-Q Depth extraction
Image-based rendering with dynamic
depth
TOPOLOGY INFERENCE & LOCAL-TO-GLOBAL ALIGNMENT
SPHERICAL MOSAICS
[Sawhney,Hsu,Kumar ECCV98, Szeliski,Shum SIGGRAPH98]
Sarnoff Library Video
Captures almost the complete sphere
with 380 frames
SPHERICAL TOPOLOGY EVOLUTION
SPHERICAL MOSAIC
Sarnoff Library
ACTIVE FOCUS OF ATTENTION
WFOV/NFOV CONTROL
DYNAMIC MOSAICS
Original Video
Video Stream with
deleted moving object
Dynamic Mosaic Video
SYNOPISIS MOSAICS
ALIGNMENT & SYNTHESIS FOR HI-RES STEREO SYNTHESIS
A HIGH END APPLICATION OF IBMR
[Sawhney,Guo,Hanna,Kumar,Zhou,Adkins SIGGRAPH2001]
Low-Res Left
Synthesized High-Res Left
Original High-Res Right
THE PROBLEM SCENARIO
INPUT
Left Eye
(Typically 1.5K)
Right Eye
(Typically 6K)
OUTPUT
3D & Motion Alignment Based Stereo Sequence
Processing
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stereo
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Left
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stereo
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Right
Left
• Highlights :
– Scintillation effect is reduced.
– Occlusion regions are better handled.
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Right
SYNTHESIS RESULT ON REAL FOOTAGE
IMPLICATIONS FOR IMMERSIVE IBMR
CAMERA CONFIGURATIONS
Lo-res camera
Hi-res camera
Multi-resolution camera configuration allows 3D capture at the highest resolution
as well as user-controlled large range of zooms without the need for
zoom control on the cameras.
Model-Centric Video Visualization
OR
Video-Centric Model Visualization
[Hsu,Supun,Kumar,Sawhney CVPR00]
Original
Video
Site
model
Georegistration
of video to
site model
Re-projection of video after merging
with model.
Video to Site Model Alignment
• Model to frame alignment
REFINE
Correspondence-less
exterior orientation
from 3D-2D line pairs
Oriented Energy Pyramid
• Goal: representation which indicates edge
strength in the image at various
orientations and scales
• Orientation selectivity: reduce false
matches
• Coarse-to-fine: increase capture range
0°
45°
90°
135°
Pose Refinement Algorithm
…iterative coarse to fine adjustment of pose ...
This will be an animation of
the gradual improvement of alignment
during the coarse to fine
iterations
regsite_animation.avi
Geo-Registration
Video to Reference Database Alignment
[Wildes et al. ICCV01]
Current Video
3D Reference Imagery
Registration : Radical Appearance Changes
Dynamic 3D Capture & Rendering
…global modeling is not feasible...
• Recovering depth from local views
• Depth refinement across multiple local views
• New view synthesis using multiple local views
Cross view depth checking
3D Shape/Depth Estimation from Multiple
Views of a Scene
Stereo Pair
• Estimation of high quality, artifact free depth maps coregistered with video imagery for rendering new views.
• Must work both outdoors and indoors
Multi-baseline depth estimation - requirements
[Tao,Sawhney,Kumar WACV00, ICCV01]
Accurate
boundaries
Accurate
boundaries
Thin
structures
Depth maps
New view
rendering
A traditional stereo algorithm
Global matching method
New view rendering using local depth estimation
Local
flow
estimation
(1992)
Color
segmentation
based stereo
algorithm
(2000)
Multiwindow
plane+
parallax
algorithm
(1998)
New view
rendering
Main ideas
• Motivations
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be able to handle textureless regions
handle object boundaries accurately
global visibility constraints should be enforced
Hypothesize reasonable depths for unmatched regions
• Solutions
– Global matching method - an analysis-by-synthesis approach
– Representation - smooth depth representation in homogeneous
region
– Search method - neighborhood depth hypotheses generation
– Efficient algorithm - incremental warping
– Scene constraints - prior functions
Color Segmentation
Original image (frame 12)
Original image (left)
Color segmentation [Comanicius 97]
New view rendering using local depth estimation
Left image
Color segmentation based stereo algorithm
True depth
new view rendering
Depth computation from 3 views
Video frame 11
Video frame 12
Color segmentation (frame 12)
Video frame 13
Depth map (frame 12)
Multiple View Depth Recovery and New View
Rendering
New view rendering from a single view. left: from frame 212, right: from frame 215
New view rendering from multiple views.
Multiple view depth recovery and new view
rendering
Original 14 video frames (frame 04-17)
New view rendering (71 frames)
Depth map of frame 12 and 15
Immersive Visualization of a Dynamic Event
• Temporally consistent motion and 3D shape extraction
• Scintillation free dynamic high-quality rendering
AN IMMERSIVE IBMR GRAND CHALLENGE
AND IF WE DO IT RIGHT
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2D & 3D VIDEO PROCESSING FOR IMMERSIVE APPLICATIONS