Raskar Next Billion Cameras Siggraph 2009

Camera Culture Ramesh Raskar Alyosha Efros Ramesh Raskar Steve Seitz Siggraph 2009 Curated Course Next Billion Cameras http://raskar.scripts.mit.edu / nextbillioncameras /

A. Introduction‐‐5 minutes
B. Cameras of the future ( Raskar , 30 minutes) * Form factors, Modalities and Interaction * Enabling Visual Social Computing
C. Reconstruction the World ( Seitz , 30 minutes) * Photo tourism and beyond * Image‐based modeling and rendering on a massive scale * Scene summarization
D. Understanding a Billion Photos ( Efros , 30 minutes) * What will the photos depict? * Photos as visual content for computer graphics * Solving computer vision
E. Discussion‐‐10 minutes
Next Billion Cameras

Alexei (Alyosha) Efros [CMU]
Assistant professor at the Robotics Institute and the Computer Science Department at Carnegie Mellon University .
His research is in the area of computer vision and computer graphics, especially at the intersection of the two. He is particularly interested in using data-driven techniques to tackle problems which are very hard to model parametrically but where large quantities of data are readily available. Alyosha received his PhD in 2003 from UC Berkeley and spent the following year as a post-doctoral fellow in Oxford, England. Alyosha is a recipient of the NSF CAREER award (2006), the Sloan Fellowship (2008), the Guggenheim Fellowship (2008), and the Okawa Grant (2008).
http://www.cs.cmu.edu/~efros/

Ramesh Raskar [MIT]
Associate Professor at the MIT Media Lab and heads the Camera Culture research group.
The group focuses on creating a new class for imaging platforms to better capture and share the visual experience. This research involves developing novel cameras with unusual optical elements, programmable illumination, digital wavelength control, and femtosecond analysis of light transport, as well as tools to decompose pixels into perceptually meaningful components.
Raskar is a receipient of Alfred P Sloan research fellowship 2009, the TR100 Award 2004, Global Indus Technovator Award 2003. He holds 35 US patents and has received four Mitsubishi Electric Invention Awards. He is currently co-authoring, with Jack Tumblin, a book on computational photography.
http://www.media.mit.edu/~raskar

Steve Seitz [U-Washington]
Professor in the Department of Computer Science and Engineering at the University of Washington.
He received Ph.D. in computer sciences at the University of Wisconsin, Madison in 1997. He was twice awarded the David Marr Prize for the best paper at the International Conference of Computer Vision, and has received an NSF Career Award, an ONR Young Investigator Award, and an Alfred P. Sloan Fellowship. His work on Photo Tourism (joint with Noah Snavely and Rick Szeliski) formed the basis of Microsoft's Photosynth technology. Professor Seitz is interested in problems in computer vision and computer graphics. His current research focuses on capturing the structure, appearance, and behavior of the real world from digital imagery.
http://www.cs.washington.edu/homes/seitz/

Where are the ‘camera’s?

Camera Culture Ramesh Raskar Alyosha Efros Ramesh Raskar Steve Seitz Siggraph 2009 Course Next Billion Cameras http://raskar.info/photo/

Camera Culture Ramesh Raskar Alyosha Efros Ramesh Raskar Steve Seitz Siggraph 2009 Course Next 100 Billion Cameras http://raskar.info/photo/

Key Message
Cameras will not look like anything today
Emerging optics, illumination, novel sensors
Visual Experience will differ from viewfinder
Photos will be ‘computed’
Remarkable post-capture control
Crowdsource the photo collection
Exploit priors and online collections
Visual Essence will dominate
Superior Metadata tagging for effective sharing
Fusion with non-visual data

Can you look around a corner ?

Can you decode a 5 micron feature from 3 meters away with an ordinary camera ?

Convert LCD into a big flat camera? Beyond Multi-touch

Pantheon

How do we move through a space?

What is ‘interesting’ here?

Record what you ‘feel’ not what you ‘see’

Camera Culture Ramesh Raskar Ramesh Raskar Camera Culture http://raskar.scripts.mit.edu / nextbillioncameras /

“ Visual Social Computing”
Social Computing (SoCo)
Computing
by the people,
for the people,
of the people
Visual SoCo
Participatory, Collaborative
Visual semantics
http://raskar.scripts.mit.edu / nextbillioncameras
?

Crowdsourcing http://www.wired.com/wired/archive/14.06/crowds.html Object Recognition Fakes Template matching Amazon Mechanical Turk: Steve Fossett search ReCAPTCHA=OCR

Participatory Urban Sensing
Deborah Estrin et al
Static/semi-dynamic/dynamic data
A. City Maintenance
Side Walks
B. Pollution
-Sensor network
C. Diet, Offenders
Graffiti
Bicycle on sidewalk
Future ..
Citizen Surveillance Health Monitoring
http://research.cens.ucla.edu/areas/2007/Urban_Sensing/ (Erin Brockovich) n

Community Photo Collections U of Washington/Microsoft: Photosynth

Beyond Visible Spectrum Cedip RedShift

Trust in Images From Hany Farid

Trust in Images From Hany Farid LA Times March’03

Cameras in Developing Countries http://news.bbc.co.uk/2/hi/south_asia/7147796.stm Community news program run by village women

Vision thru tongue http://www.pbs.org/kcet/wiredscience/story/97-mixed_feelings.html Solutions for the Visually Challenged http://www.seeingwithsound.com/

New Topics in Imaging Research
Imaging Devices, Modern Optics and Lenses
Emerging Sensor Technologies
Mobile Photography
Visual Social Computing and Citizen Journalism
Imaging Beyond Visible Spectrum
Computational Imaging in Sciences (Medical)
Trust in Visual Media
Solutions for Visually Challenged
Cameras in Developing Countries
Social Stability, Commerce and Governance
Future Products and Business Models

Traditional Photography Lens Detector Pixels Image Mimics Human Eye for a Single Snapshot : Single View, Single Instant, Fixed Dynamic range and Depth of field for given Illumination in a Static world Courtesy: Shree Nayar

Computational Photography Computational Illumination Computational Camera Scene : 8D Ray Modulator Display Generalized Sensor Generalized Optics Processing 4D Ray Bender Upto 4D Ray Sampler Ray Reconstruction Generalized Optics Recreate 4D Lightfield Light Sources Modulators 4D Incident Lighting 4D Light Field

Computational Photography [Raskar and Tumblin]
Epsilon Photography
Low-level vision: Pixels
Multi-photos by perturbing camera parameters
HDR, panorama, …
‘ Ultimate camera’
Coded Photography
Mid-Level Cues:
Regions, Edges, Motion, Direct/global
Single/few snapshot
Reversible encoding of data
Additional sensors/optics/illum
‘ Scene analysis’
Essence Photography
High-level understanding
Not mimic human eye
Beyond single view/illum
‘ New artform’
captures a machine-readable representation of our world to hyper-realistically synthesize the essence of our visual experience.

Goal and Experience Low Level Mid Level High Level Hyper realism Raw Angle, spectrum aware Non-visual Data, GPS Metadata Priors Comprehensive 8D reflectance field Digital Epsilon Coded Essence Computational Photography aims to make progress on both axis Camera Array HDR, FoV Focal stack Decomposition problems Depth Spectrum LightFields Human Stereo Vision Transient Imaging Virtual Object Insertion Relighting Augmented Human Experience Material editing from single photo Scene completion from photos Motion Magnification Phototourism

2 nd International Conference on Computational Photography Papers due November 2, 2009 http://cameraculture.media.mit.edu/iccp10

Ramesh Raskar and Jack Tumblin
Book Publishers: A K Peters
Siggraph 2009 booth: 20% off
Booth #2527
ComputationalPhotography.org
Meet the Authors
Thursday at 2pm-2:30pm

Epsilon Photography
Low-level vision: Pixels
Multi-photos by perturbing camera parameters
HDR, panorama, …
Coded Photography
Single/few snapshot
‘ Scene analysis’ : (Consumer software?)
Essence Photography
Not mimic human eye
‘ New art form’

Epsilon Photography
Dynamic range
Exposure bracketing [Mann-Picard, Debevec]
Wider FoV
Stitching a panorama
Depth of field
Fusion of photos with limited DoF [Agrawala04]
Noise
Flash/no-flash image pairs
Frame rate
Triggering multiple cameras [Wilburn04]

Dynamic Range Goal: High Dynamic Range Short Exposure Long Exposure

Epsilon Photography
Dynamic range
Exposure braketing [Mann-Picard, Debevec]
Wider FoV
Stitching a panorama
Depth of field
Fusion of photos with limited DoF [Agrawala04]
Noise
Flash/no-flash image pairs [ Petschnigg04, Eisemann04]
Frame rate
Triggering multiple cameras [Wilburn05, Shechtman02]

Computational Photography
Epsilon Photography
Low-level Vision: Pixels
Multiphotos by perturbing camera parameters
HDR, panorama
Coded Photography
Mid-Level Cues:
Regions, Edges, Motion, Direct/global
Single/few snapshot
‘ Scene analysis’
Essence Photography
Not mimic human eye
‘ New artform’

3D
Stereo of multiple cameras
Higher dimensional LF
Light Field Capture
lenslet array [Adelson92, Ng05] , ‘3D lens’ [Georgiev05] , heterodyne masks [Veeraraghavan07]
Boundaries and Regions
Multi-flash camera with shadows [Raskar08]
Fg/bg matting [Chuang01,Sun06]
Deblurring
Engineered PSF
Motion: Flutter shutter [Raskar06] , Camera Motion [Levin08]
Defocus: Coded aperture [Veeraraghavan07,Levin07] , Wavefront coding [Cathey95]
Global vs direct illumination
High frequency illumination [Nayar06]
Glare decomposition [Talvala07, Raskar08]
Coded Sensor
Gradient camera [Tumblin05]

Digital Refocusing using Light Field Camera 125 μ square-sided microlenses [Ng et al 2005]

3D
Light Field Capture
Deblurring
Engineered PSF
Coded Sensor

Multi-flash Camera for Detecting Depth Edges

Depth Edges Left Top Right Bottom Depth Edges Canny Edges

3D
Light Field Capture
Deblurring
Engineered PSF
Coded Sensor

Flutter Shutter Camera Raskar, Agrawal, Tumblin [Siggraph2006] LCD opacity switched in coded sequence

Traditional Coded Exposure Image of Static Object Deblurred Image Deblurred Image

3D
Light Field Capture
Deblurring
Engineered PSF
Decomposition Problems
High frequency illumination, Global/direct illumination [Nayar06]
Coded Sensor

"Fast Separation of Direct and Global Components of a Scene using High Frequency Illumination," S.K. Nayar, G. Krishnan, M. D. Grossberg, R. Raskar, ACM Trans. on Graphics (also Proc. of ACM SIGGRAPH), Jul, 2006.

Epsilon Photography
Multiphotos by varying camera parameters
HDR, panorama
‘ Ultimate camera’ : (Photo-editor)
Coded Photography
Single/few snapshot
‘ Scene analysis’ : (Next software?)
Essence Photography
High-level understanding
Not mimic human eye
‘ New artform’

Blind Camera Sascha Pohflepp, U of the Art, Berlin, 2006

Capturing the Essence of Visual Experience
Exploiting online collections
Photo-tourism [Snavely2006]
Scene Completion [Hays2007]
Multi-perspective Images
Multi-linear Perspective [Jingyi Yu, McMillan 2004]
Unwrap Mosaics [Rav-Acha et al 2008]
Video texture panoramas [Agrawal et al 2005]
Non-photorealistic synthesis
Motion magnification [Liu05]
Image Priors
Learned features and natural statistics
Face Swapping: [Bitouk et al 2008]
Data-driven enhancement of facial attractiveness [Leyvand et al 2008]
Deblurring [Fergus et al 2006, Several 2008 and 2009 papers]

Scene Completion Using Millions of Photographs Hays and Efros, Siggraph 2007

Community Photo Collections U of Washington/Microsoft: Photosynth

Can you look around a corner ?

Can you look around a corner ? Kirmani, Hutchinson, Davis, Raskar 2009 Accepted for ICCV’2009, Oct 2009 in Kyoto Impulse Response of a Scene

Femtosecond Laser as Light Source Pico-second detector array as Camera

Coded Aperture Camera The aperture of a 100 mm lens is modified Rest of the camera is unmodified Insert a coded mask with chosen binary pattern

In Focus Photo LED

Out of Focus Photo: Open Aperture

Out of Focus Photo: Coded Aperture

Captured Blurred Photo

Refocused on Person

Smart Barcode size : 3mm x 3mm
Ordinary Camera: Distance 3 meter
Computational Probes: Long Distance Bar-codes Mohan, Woo,Smithwick, Hiura, Raskar Accepted as Siggraph 2009 paper

Bokode

Barcodes markers that assist machines in understanding the real world

Bokode: ankit mohan, grace woo, shinsaku hiura, quinn smithwick, ramesh raskar camera culture group, MIT media lab imperceptible visual tags for camera based interaction from a distance

Defocus blur of Bokode

Image greatly magnified. Simplified Ray Diagram

Our Prototypes

street-view tagging

Converting LCD Screen = large Camera for 3D Interactive HCI and Video Conferencing Matthew Hirsch, Henry Holtzman Doug Lanman, Ramesh Raskar BiDi Screen *

Beyond Multi-touch: Mobile Laptops Mobile

Light Sensing Pixels in LCD Display with embedded optical sensors Sharp Microelectronics Optical Multi-touch Prototype

Design Overview Display with embedded optical sensors LCD , displaying mask Optical sensor array ~2.5 cm ~50 cm

Beyond Multi-touch: Hover Interaction
Seamless transition of multitouch to gesture
Thin package, LCD

Design Vision Object Collocated Capture and Display Bare Sensor Spatial Light Modulator

Touch + Hover using Depth Sensing LCD Sensor

Overview: Sensing Depth from Array of Virtual Cameras in LCD

Visual Social Computing
Digital
Epsilon
Coded
Essence
Beyond Traditional Imaging
Looking around a corner
LCDs as virtual cameras
Computational probes (bokode)
Camera Culture Group, MIT Media Lab Ramesh Raskar http://raskar.info Cameras of the Future Digital Epsilon Coded Essence Computational Photography aims to make progress on both axis Camera Array HDR, FoV Focal stack Decomposition problems Depth Spectrum LightFields Human Stereo Vision Transient Imaging Virtual Object Insertion Relighting Augmented Human Experience Material editing from single photo Scene completion from photos Motion Magnification Phototourism

Camera Culture Ramesh Raskar Alyosha Efros Ramesh Raskar Steve Seitz Siggraph 2009 Course Next Billion Cameras http://raskar.info/photo/

Capture
Overcome Limitations of Cameras
Capture Richer Data
Multispectral
New Classes of Visual Signals
Lightfields, Depth, Direct/Global, Fg/Bg separation
Hyperrealistic Synthesis
Post-capture Control
Impossible Photos
Close to Scientific Imaging
Computational Photography http://raskar.info/photo/

Questions
What will a camera look like in 10,20 years?
How will a billion networked and portable cameras change the social culture?
How will online photo collections transform visual social computing?
How will movie making/new reporting change?
computational-journalism.com

Fernald, Science [Sept 2006] Shadow Refractive Reflective Tools for Visual Computing

Cameras and their Impact
Beyond Traditional Imaging Analysis and synthesis
Emerging optics, illumination, novel sensors
Exploit priors and online collections
Applications
Better scene understanding/analysis
Capture visual essence
Superior Metadata tagging for effective sharing
Fuse non-visual data
Impact on Society
Beyond entertainment and productivity
Sensors for disabled, new art forms, crowdsourcing, bridging cultures, social stability

2 nd International Conference on Computational Photography Papers due November 2, 2009 http://cameraculture.media.mit.edu/iccp10

Ramesh Raskar and Jack Tumblin
Book Publishers: A K Peters
Siggraph 2009 booth: 20% off
Booth #2527
ComputationalPhotography.org
Meet the Authors
Thursday at 2pm-2:30pm

Visual Social Computing
Digital
Epsilon
Coded
Essence
Beyond Traditional Imaging
Looking around a corner
LCDs as virtual cameras
Computational probes (bokode)
Next Billion Cameras Digital Epsilon Coded Essence Computational Photography aims to make progress on both axis Camera Array HDR, FoV Focal stack Decomposition problems Depth Spectrum LightFields Human Stereo Vision Transient Imaging Virtual Object Insertion Relighting Augmented Human Experience Material editing from single photo Scene completion from photos Motion Magnification Phototourism

A. Cameras of the future ( Raskar , 30 minutes) * Enabling Visual Social Computing * Computational Photography * Beyond Traditional Imaging
B. Reconstruction the World ( Seitz , 30 minutes) * Photo tourism and beyond * Image‐based modeling and rendering on a massive scale * Scene summarization
C. Understanding a Billion Photos ( Efros , 30 minutes) * What will the photos depict? * Photos as visual content for computer graphics * Solving computer vision
Course Evaluation (prize: free mug for each course!) http://www.siggraph.org/courses_evaluation IntConf on Computational Photography, Mar’2010 Papers due Nov 2, 2009 http://cameraculture.info/iccp10 Book: Computational Photography [Raskar and Tumblin] AkPeters Booth #2527, 20% coupons here, Meet Authors Thu 2pm

Raskar Next Billion Cameras Siggraph 2009

by MIT Media Lab, Camera Culture group on Aug 14, 2009

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Raskar Next Billion Cameras Siggraph 2009 — Presentation Transcript