Raskar Next Billion Cameras Siggraph 2009

Raskar, Camera Culture, MIT Media Lab

Camera Culture

Ramesh Raskar

Alyosha Efros Ramesh Raskar

Steve Seitz

Siggraph 2009 Curated CourseNext 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]Alexei (Alyosha) Efros [CMU]Assistant professor at the Robotics Institute and Assistant professor at the Robotics Institute and

the Computer Science Department at the Computer Science Department at Carnegie Carnegie Mellon UniversityMellon University. .

His research is in the area of computer vision and His research is in the area of computer vision and computer graphics, especially at the computer graphics, especially at the intersection of the two. He is particularly intersection of the two. He is particularly interested in using data-driven techniques to interested in using data-driven techniques to tackle problems which are very hard to model tackle problems which are very hard to model parametrically but where large quantities of parametrically but where large quantities of data are readily available. Alyosha received his data are readily available. Alyosha received his PhD in 2003 from UC Berkeley and spent the PhD in 2003 from UC Berkeley and spent the following year as a post-doctoral fellow in following year as a post-doctoral fellow in Oxford, England. Alyosha is a recipient of the Oxford, England. Alyosha is a recipient of the NSF CAREER award (2006), the Sloan NSF CAREER award (2006), the Sloan Fellowship (2008), the Guggenheim Fellowship Fellowship (2008), the Guggenheim Fellowship (2008), and the Okawa Grant (2008). (2008), and the Okawa Grant (2008).

http://www.cs.cmu.edu/~efros/http://www.cs.cmu.edu/~efros/

Ramesh Raskar [MIT] Ramesh Raskar [MIT] Associate Professor at the Associate Professor at the MIT Media Lab MIT Media Lab and and

heads the Camera Culture research group. heads the Camera Culture research group.

The group focuses on creating a new class for The group focuses on creating a new class for imaging platforms to better capture and share imaging platforms to better capture and share the visual experience. This research involves the visual experience. This research involves developing novel cameras with unusual optical developing novel cameras with unusual optical elements, programmable illumination, digital elements, programmable illumination, digital wavelength control, and femtosecond analysis wavelength control, and femtosecond analysis of light transport, as well as tools to of light transport, as well as tools to decompose pixels into perceptually meaningful decompose pixels into perceptually meaningful components. components.

Raskar is a receipient of Alfred P Sloan research Raskar is a receipient of Alfred P Sloan research fellowship 2009, the TR100 Award 2004, Global fellowship 2009, the TR100 Award 2004, Global Indus Technovator Award 2003. He holds 35 US Indus Technovator Award 2003. He holds 35 US patents and has received four Mitsubishi patents and has received four Mitsubishi Electric Invention Awards. He is currently co-Electric Invention Awards. He is currently co-authoring, with Jack Tumblin, a book on authoring, with Jack Tumblin, a book on computational photography.computational photography.

http://www.media.mit.edu/~raskar http://www.media.mit.edu/~raskar

Steve Seitz [U-Washington]Steve Seitz [U-Washington]Professor in the Department of Computer Science Professor in the Department of Computer Science

and Engineering at the and Engineering at the University of University of Washington. Washington.

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

http://www.cs.washington.edu/homes/seitz/http://www.cs.washington.edu/homes/seitz/

Where are the ‘camera’s?Where are the ‘camera’s?


Camera Culture

Ramesh Raskar


Steve Seitz

Siggraph 2009 Course


http://raskar.info/photo/




Camera Culture

Ramesh Raskar


Steve Seitz

Siggraph 2009 Course

Next 100 Billion Cameras




Key MessageKey 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 Can you decode a 5 micron feature from 3 meters away from 3 meters away

with an ordinary camera ?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”Visual Social Computing”• Social Computing (SoCo)Social Computing (SoCo)

– Computing Computing by the people, by the people, for the people, for the people, of the people of the people

• Visual SoCoVisual SoCo– Participatory, CollaborativeParticipatory, Collaborative– Visual semanticsVisual semantics– http://raskar.scripts.mit.edu / nextbillioncamerashttp://raskar.scripts.mit.edu / nextbillioncameras

?

CrowdsourcingCrowdsourcing

http://www.wired.com/wired/archive/14.06/crowds.html

Object RecognitionFakesTemplate matching

Amazon Mechanical Turk: Steve Fossett search

ReCAPTCHA=OCR

Participatory Urban SensingParticipatory 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 SurveillanceHealth Monitoring

http://research.cens.ucla.edu/areas/2007/Urban_Sensing/

(Erin Brockovich)n

Community Photo CollectionsCommunity Photo Collections U of Washington/Microsoft: Photosynth

Beyond Visible SpectrumBeyond Visible Spectrum

CedipRedShift

Trust in ImagesTrust in Images

From Hany Farid

Trust in ImagesTrust in Images

From Hany Farid

LA Times March’03

Cameras in Developing CountriesCameras in Developing Countries

http://news.bbc.co.uk/2/hi/south_asia/7147796.stm

Community news program run by village women

Vision thru tongueVision thru tongue

http://www.pbs.org/kcet/wiredscience/story/97-mixed_feelings.html

Solutions for the Visually ChallengedSolutions for the Visually Challenged

http://www.seeingwithsound.com/

New Topics in Imaging ResearchNew Topics in Imaging Research

• Imaging Devices, Modern Optics and LensesImaging Devices, Modern Optics and Lenses• Emerging Sensor TechnologiesEmerging Sensor Technologies• Mobile PhotographyMobile Photography• Visual Social Computing and Citizen JournalismVisual Social Computing and Citizen Journalism• Imaging Beyond Visible SpectrumImaging Beyond Visible Spectrum• Computational Imaging in Sciences (Medical)Computational Imaging in Sciences (Medical)• Trust in Visual MediaTrust in Visual Media• Solutions for Visually ChallengedSolutions for Visually Challenged• Cameras in Developing CountriesCameras in Developing Countries

– Social Stability, Commerce and GovernanceSocial Stability, Commerce and Governance• Future Products and Business ModelsFuture Products and Business Models

Traditional PhotographyTraditional 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 Illumination

Computational Camera

Scene: 8D Ray Modulator

Display

GeneralizedSensor

Generalized OpticsProcessing

4D Ray BenderUpto 4D

Ray Sampler

Ray Reconstruction

Generalized Optics

Recreate 4D Lightfield

Light Sources

Modulators

4D Incident Lighting

4D Light Field

Computational PhotographyComputational Photography

Computational Photography [Raskar and Tumblin]

1. Epsilon Photography– Low-level vision: Pixels– Multi-photos by perturbing camera parameters– HDR, panorama, …– ‘Ultimate camera’

2. Coded Photography– Mid-Level Cues:

• Regions, Edges, Motion, Direct/global– Single/few snapshot

• Reversible encoding of data– Additional sensors/optics/illum– ‘Scene analysis’

3. Essence Photography– High-level understanding

• Not mimic human eye• Beyond single view/illum

– ‘New artform’

captures a machine-readable representation of our world tohyper-realistically synthesize the essence of our visual experience.

Goal and Experience

Low Level Mid Level HighLevel

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 ArrayHDR, 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

2nd 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

Computational Photography[Raskar and Tumblin]

1. Epsilon Photography– Low-level vision: Pixels– Multi-photos by perturbing camera parameters– HDR, panorama, …– ‘Ultimate camera’

2. Coded Photography– Single/few snapshot– Reversible encoding of data– Additional sensors/optics/illum– ‘Scene analysis’ : (Consumer software?)

3. Essence Photography– Beyond single view/illum– Not mimic human eye– ‘New art form’

Epsilon PhotographyEpsilon 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]

Goal: High Dynamic RangeGoal: High Dynamic Range

Short ExposureShort Exposure

Long ExposureLong Exposure

Dynamic Range

Epsilon PhotographyEpsilon 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 PhotographyComputational Photography

1. Epsilon Photography– Low-level Vision: Pixels– Multiphotos by perturbing camera parameters– HDR, panorama– ‘Ultimate camera’

2. Coded Photography– Mid-Level Cues:

• Regions, Edges, Motion, Direct/global– Single/few snapshot

• Reversible encoding of data– Additional sensors/optics/illum– ‘Scene analysis’

3. Essence Photography– Not mimic human eye– Beyond single view/illum– ‘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]

Marc Levoy

Digital Refocusing using Light Field Camera

125μ square-sided microlenses[Ng et al 2005]

Multi-flash Camera for Detecting Depth Edges

Depth Depth EdgesEdges

LeftLeft TopTop RightRight BottomBottom

Depth EdgesDepth EdgesCanny EdgesCanny Edges

Flutter Shutter CameraFlutter Shutter CameraRaskar, Agrawal, Tumblin Raskar, Agrawal, Tumblin

[Siggraph2006][Siggraph2006]

LCD opacity switched LCD opacity switched in coded sequencein coded sequence

TraditioTraditionalnal

Coded Coded ExposuExposu

rere

Image of Image of Static Static ObjectObject

Deblurred Deblurred ImageImage

Deblurred Deblurred ImageImage








• Decomposition Problems– High frequency illumination, Global/direct illumination [Nayar06]



"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.

Computational Photography [Raskar and Tumblin]

1. Epsilon Photography– Multiphotos by varying camera parameters– HDR, panorama– ‘Ultimate camera’: (Photo-editor)

2. Coded Photography– Single/few snapshot– Reversible encoding of data– Additional sensors/optics/illum– ‘Scene analysis’ : (Next software?)

3. Essence Photography– High-level understanding

• Not mimic human eye• Beyond single view/illum

– ‘New artform’

Blind CameraBlind Camera

Sascha Pohflepp, Sascha Pohflepp, U of the Art, Berlin, 2006U 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 PhotographsHays and Efros, Siggraph 2007

Community Photo Collections U of Washington/Microsoft: Photosynth


Kirmani, Hutchinson, Davis, Raskar 2009Accepted for ICCV’2009, Oct 2009 in Kyoto

Impulse Response of a Scene

Femtosecond Laser as Light SourcePico-second detector array as

Camera

Coded Aperture CameraCoded Aperture Camera

The aperture of a 100 mm lens is modified

Rest of the camera is unmodifiedInsert a coded mask with chosen binary pattern

Captured Blurred Photo

Refocused on Person

• Smart Barcode size : 3mm x 3mm• Ordinary Camera: Distance 3 meter

Computational Probes: Computational Probes: Long Distance Bar-codesLong Distance Bar-codes

Mohan, Woo,Smithwick, Hiura, RaskarAccepted as Siggraph 2009 paper

MIT Media Lab Camera Culture

Bokode

MIT media lab camera culture

Barcodesmarkers 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 HoltzmanDoug Lanman, Ramesh Raskar

BiDi Screen*

Beyond Multi-touch: Mobile

Laptops

Mobile

Light Sensing Pixels in LCD

Dis

play

with

em

bedd

ed o

ptic

al s

enso

rs

Sharp Microelectronics Optical Multi-touch Prototype

Design Overview

Dis

play

with

em

bedd

ed o

ptic

al s

enso

rs

LCD , displaying mask

Opt

ical

sen

sor a

rray

~2.5 cm~50 cm

Beyond Multi-touch: Hover Interaction

• Seamless transition of multitouch to gesture

• Thin package, LCD

Design Vision

Object Collocated Captureand Display

Bare Sensor

Spa

tial L

ight

Mod

ulat

or

Touch + Hover using Depth Sensing LCD Sensor

Overview: Sensing Depth from Array of Virtual Cameras in

LCD

Camera Culture Group, MIT Media Lab Ramesh Raskar http://raskar.info

• Visual Social Computing

• Computational Photography• Digital• Epsilon• Coded• Essence

• Beyond Traditional Imaging• Looking around a corner• LCDs as virtual cameras• Computational probes (bokode)

Cameras of the Future

Digital

Epsilon

Coded

Essence

Computational Photography aims to make progress on both axis

Camera Array

HDR, FoV Focal stack


Depth

Spectrum

LightFields

Human Stereo Vision

Transient Imaging


Relighting

Augmented Human

Experience

Material editing from single

photo

Scene completion from

photos


Phototourism


Camera Culture

Ramesh Raskar


Steve Seitz

Siggraph 2009 CourseNext Billion Cameras




CaptureOvercome Limitations of Cameras

Capture Richer DataMultispectral

New Classes of Visual SignalsLightfields, Depth, Direct/Global, Fg/Bg separation

Hyperrealistic Synthesis

Post-capture Control

Impossible Photos

Close to Scientific Imaging

Computational Photographyhttp://raskar.info/photo/

http://raskar.scripts.mit.edu / nextbillioncamerashttp://raskar.scripts.mit.edu / nextbillioncameras


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 Computin

g

Cameras and their ImpactCameras 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

2nd 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

http://raskar.scripts.mit.edu / nextbillioncamerashttp://raskar.scripts.mit.edu / nextbillioncameras

• Visual Social Computing

• Computational Photography• Digital• Epsilon• Coded• Essence

• Beyond Traditional Imaging• Looking around a corner• LCDs as virtual cameras• Computational probes (bokode)


Digital

Epsilon

Coded

Essence

Computational Photography aims to make progress on both axis

Camera Array

HDR, FoV Focal stack


Depth

Spectrum

LightFields

Human Stereo Vision

Transient Imaging


Relighting

Augmented Human

Experience

Material editing from single

photo

Scene completion from

photos


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

Next Billion Camerashttp://raskar.scripts.mit.edu / nextbillioncamerashttp://raskar.scripts.mit.edu / nextbillioncameras

Course Evaluation (prize: free mug for each course!) http://www.siggraph.org/courses_evaluation

IntConf on Computational Photography, Mar’2010Papers due Nov 2, 2009 http://cameraculture.info/iccp10Book: Computational Photography [Raskar and Tumblin]

AkPeters Booth #2527, 20% coupons here, Meet Authors Thu 2pm

Raskar Next Billion Cameras Siggraph 2009

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Transcript of Raskar Next Billion Cameras Siggraph 2009