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Many-Camera Systems: How They Started at CMU up to EyeVision at 2001 Superbowl CVPR 2017 in Honolulu Takeo Kanade Robotics Institute Carnegie Mellon University

Many-Camera Systems - CMU Panoptic Datasetdomedb.perception.cs.cmu.edu/tutorials/.../slides/invited1_Kanade.pdf · Many-Camera Systems: How They Started at CMU up to EyeVision at

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Page 1: Many-Camera Systems - CMU Panoptic Datasetdomedb.perception.cs.cmu.edu/tutorials/.../slides/invited1_Kanade.pdf · Many-Camera Systems: How They Started at CMU up to EyeVision at

Many-Camera Systems:

How They Started at CMU up to EyeVision at 2001 Superbowl

CVPR 2017 in Honolulu

Takeo Kanade Robotics Institute

Carnegie Mellon University

Page 2: Many-Camera Systems - CMU Panoptic Datasetdomedb.perception.cs.cmu.edu/tutorials/.../slides/invited1_Kanade.pdf · Many-Camera Systems: How They Started at CMU up to EyeVision at

Video-Rate Multi-Camera Stereo Machine Project (1991-1996) for Real-time Range Mapping

Okutomi-Kanade

Multi-baseline stereo

theory 1990

Multi-camera Stereo

v.1

v.2 Outputs: Color Image 256 x 256 x 24 bits

Depth Image 200 x 200 x 8 bits at 30 fps

Page 3: Many-Camera Systems - CMU Panoptic Datasetdomedb.perception.cs.cmu.edu/tutorials/.../slides/invited1_Kanade.pdf · Many-Camera Systems: How They Started at CMU up to EyeVision at

Soft Camera (aka Virtual Camera):

Occlusion Problem

Range

Intensity/Color

View Synthesis

Soft Camera Output

Page 4: Many-Camera Systems - CMU Panoptic Datasetdomedb.perception.cs.cmu.edu/tutorials/.../slides/invited1_Kanade.pdf · Many-Camera Systems: How They Started at CMU up to EyeVision at

Whole-Scene Modeling

We use many, many

cameras!!

Page 5: Many-Camera Systems - CMU Panoptic Datasetdomedb.perception.cs.cmu.edu/tutorials/.../slides/invited1_Kanade.pdf · Many-Camera Systems: How They Started at CMU up to EyeVision at

3D Dome Analog system

with 10 BW Cameras - circa 1994 with 51 Color Cameras - circa 1995

Page 6: Many-Camera Systems - CMU Panoptic Datasetdomedb.perception.cs.cmu.edu/tutorials/.../slides/invited1_Kanade.pdf · Many-Camera Systems: How They Started at CMU up to EyeVision at

Old and fun stories

Grad student vs. Robot

51 Analog

Video tape recorders

Page 7: Many-Camera Systems - CMU Panoptic Datasetdomedb.perception.cs.cmu.edu/tutorials/.../slides/invited1_Kanade.pdf · Many-Camera Systems: How They Started at CMU up to EyeVision at

Old and fun stories

Grad student vs. Robot

Synchronization – nontrivial problem

Page 8: Many-Camera Systems - CMU Panoptic Datasetdomedb.perception.cs.cmu.edu/tutorials/.../slides/invited1_Kanade.pdf · Many-Camera Systems: How They Started at CMU up to EyeVision at

Old and fun stories

Grad student vs. Robot

Synchronization – nontrivial problem

Our own digitizers

Page 9: Many-Camera Systems - CMU Panoptic Datasetdomedb.perception.cs.cmu.edu/tutorials/.../slides/invited1_Kanade.pdf · Many-Camera Systems: How They Started at CMU up to EyeVision at

Old and fun stories

Grad student vs. Robot

Synchronization – nontrivial problem

Our own digitizers

Calibration pattern on special non-

stretchable paper

Page 10: Many-Camera Systems - CMU Panoptic Datasetdomedb.perception.cs.cmu.edu/tutorials/.../slides/invited1_Kanade.pdf · Many-Camera Systems: How They Started at CMU up to EyeVision at

4D Full Body Surface Modeling

*

. . .

Stereo

Stereo

Stereo Merging

Texture

Map

Page 11: Many-Camera Systems - CMU Panoptic Datasetdomedb.perception.cs.cmu.edu/tutorials/.../slides/invited1_Kanade.pdf · Many-Camera Systems: How They Started at CMU up to EyeVision at

Voxel Merging

Depth Map 1 Depth Map 2

5m x 5m x 3m

1 cm3 voxel

500 x 500 x 300

Implicit function:

> 0 outside F(x,y,z) = 0 on the surface < 0 inside {

[Levoy, etal]

[Ikeuchi, etal]

[Kanade, etal]

Page 12: Many-Camera Systems - CMU Panoptic Datasetdomedb.perception.cs.cmu.edu/tutorials/.../slides/invited1_Kanade.pdf · Many-Camera Systems: How They Started at CMU up to EyeVision at

4D Modeling One on One – circa 1995

Page 13: Many-Camera Systems - CMU Panoptic Datasetdomedb.perception.cs.cmu.edu/tutorials/.../slides/invited1_Kanade.pdf · Many-Camera Systems: How They Started at CMU up to EyeVision at

Example:

3-Man Basketball

4D Digitization (1995)

Input

sequence

4D Model

Synthetic court

Page 14: Many-Camera Systems - CMU Panoptic Datasetdomedb.perception.cs.cmu.edu/tutorials/.../slides/invited1_Kanade.pdf · Many-Camera Systems: How They Started at CMU up to EyeVision at

Fully Digital “3D Room” ~2000

SynchronizationSignal Generator

Time Code Generator

TCT: Time Code Translator

TCT

TCT

TCT

S-Video

Main Memory

CPU

HDD

Digitizing PC 1

TCT

TCT

TCT

S-Video

Main Memory

CPU

HDD

Digitizing PC 2

TCT

TCT

TCT

S-Video

Main Memory

CPU

HDD

Digitizing PC n

ControlPC

New 3D Room ~ 2003

Page 15: Many-Camera Systems - CMU Panoptic Datasetdomedb.perception.cs.cmu.edu/tutorials/.../slides/invited1_Kanade.pdf · Many-Camera Systems: How They Started at CMU up to EyeVision at

Quality of Life Technology Center

Input

sequence

(These are movies.)

Fly-Through View

4D Digitization (~2000): Man-Sofa-Ball

Digital 3D Room: 39 High Quality Cameras

Page 16: Many-Camera Systems - CMU Panoptic Datasetdomedb.perception.cs.cmu.edu/tutorials/.../slides/invited1_Kanade.pdf · Many-Camera Systems: How They Started at CMU up to EyeVision at

Real Time 4D Digitization

• 64 x 64 x 64

• 10 frames/sec (with 5 PCs)

• Avatar creation

(These are movies.)

Page 17: Many-Camera Systems - CMU Panoptic Datasetdomedb.perception.cs.cmu.edu/tutorials/.../slides/invited1_Kanade.pdf · Many-Camera Systems: How They Started at CMU up to EyeVision at

Example 5:

Dance (Aug 2000)

4D Digitization

Click to play

Page 18: Many-Camera Systems - CMU Panoptic Datasetdomedb.perception.cs.cmu.edu/tutorials/.../slides/invited1_Kanade.pdf · Many-Camera Systems: How They Started at CMU up to EyeVision at

Markerless Motion Transfer

Input

video

martial arts master

kung-fu fighting

ballet expert

dancing

Motion

Tracking

Motion

Rendering Processing

Kinematics

Modeling

Output

video

ballet expert

kung-fu fighting

martial arts master

dancing

Page 19: Many-Camera Systems - CMU Panoptic Datasetdomedb.perception.cs.cmu.edu/tutorials/.../slides/invited1_Kanade.pdf · Many-Camera Systems: How They Started at CMU up to EyeVision at

SubjectE performs SubjectS’s THROW motion

Page 20: Many-Camera Systems - CMU Panoptic Datasetdomedb.perception.cs.cmu.edu/tutorials/.../slides/invited1_Kanade.pdf · Many-Camera Systems: How They Started at CMU up to EyeVision at

EyeVision

at

Super Bowl

Movie "Matrix"-like replay anywhere in the field

Page 21: Many-Camera Systems - CMU Panoptic Datasetdomedb.perception.cs.cmu.edu/tutorials/.../slides/invited1_Kanade.pdf · Many-Camera Systems: How They Started at CMU up to EyeVision at

EyeVision at Super Bowl XXXV 2001 33 robot cameras

Control Room Stadium

33 Cameras

Page 22: Many-Camera Systems - CMU Panoptic Datasetdomedb.perception.cs.cmu.edu/tutorials/.../slides/invited1_Kanade.pdf · Many-Camera Systems: How They Started at CMU up to EyeVision at

Trailer and wires

Page 23: Many-Camera Systems - CMU Panoptic Datasetdomedb.perception.cs.cmu.edu/tutorials/.../slides/invited1_Kanade.pdf · Many-Camera Systems: How They Started at CMU up to EyeVision at

EyeVision “Best of”

Page 24: Many-Camera Systems - CMU Panoptic Datasetdomedb.perception.cs.cmu.edu/tutorials/.../slides/invited1_Kanade.pdf · Many-Camera Systems: How They Started at CMU up to EyeVision at

Bigger ideas that didn’t happen?

Page 25: Many-Camera Systems - CMU Panoptic Datasetdomedb.perception.cs.cmu.edu/tutorials/.../slides/invited1_Kanade.pdf · Many-Camera Systems: How They Started at CMU up to EyeVision at

Interview Room of the Future

Multi-modal

Interactive

Real-time feedback

Page 26: Many-Camera Systems - CMU Panoptic Datasetdomedb.perception.cs.cmu.edu/tutorials/.../slides/invited1_Kanade.pdf · Many-Camera Systems: How They Started at CMU up to EyeVision at

Scale ?

Scale

(Normalized)

”Complexity”

Room Country City Campus Stadium

10 3

10 6

10 9

10 13

“1”

(1000km)2 x 1km (10m)2 x 1m (100m)2 x 10m (1km)2 x 100m (10km)2 x 100m

Page 27: Many-Camera Systems - CMU Panoptic Datasetdomedb.perception.cs.cmu.edu/tutorials/.../slides/invited1_Kanade.pdf · Many-Camera Systems: How They Started at CMU up to EyeVision at

MEMS-based

Steerable

Balloon

Cameras,

GPS,

Radio links

3D Country

Page 28: Many-Camera Systems - CMU Panoptic Datasetdomedb.perception.cs.cmu.edu/tutorials/.../slides/invited1_Kanade.pdf · Many-Camera Systems: How They Started at CMU up to EyeVision at

with Yuichi Ohta (Tsukuba)

Hideo Saito (Keio)

Akimichi (Takenaka)

Seiki Inoue (NHK)

O-ita Stadium “Free View Point” Project

Page 29: Many-Camera Systems - CMU Panoptic Datasetdomedb.perception.cs.cmu.edu/tutorials/.../slides/invited1_Kanade.pdf · Many-Camera Systems: How They Started at CMU up to EyeVision at

Stadium

Computer

Virtual Wall

Individuals

TV

3D Displays

Network

3D TV

Page 30: Many-Camera Systems - CMU Panoptic Datasetdomedb.perception.cs.cmu.edu/tutorials/.../slides/invited1_Kanade.pdf · Many-Camera Systems: How They Started at CMU up to EyeVision at

Central

Computing

Modeling

• How much computation at the center or at the TV computer ?

• Where/how the storage at the user end?

• What the most efficient intermediate representation ?

• What interactions by users ?

3D Dome Sports Arena

Network, or Broadcast

Settop Box

User’s

TV computer

Intermediate Data Representation

Offline

distributor

Computation vs. Bandwidth

Page 31: Many-Camera Systems - CMU Panoptic Datasetdomedb.perception.cs.cmu.edu/tutorials/.../slides/invited1_Kanade.pdf · Many-Camera Systems: How They Started at CMU up to EyeVision at

Computation?

Estimate:

500 x 500 x 500, 50 NTSC Cam, 30 f/s 100 Gflop

Progress:

Jan 1998: 10,000 x Realtime with a few SGIs (14 days elapse time)

Feb 1999: 1,000 x Realtime with 20 PCs (4 days elapse time)

Jan 2002: Modeling: < 50 x Realtime Display: Realtime

Page 32: Many-Camera Systems - CMU Panoptic Datasetdomedb.perception.cs.cmu.edu/tutorials/.../slides/invited1_Kanade.pdf · Many-Camera Systems: How They Started at CMU up to EyeVision at

Computation?

Estimate:

500 x 500 x 500, 50 NTSC Cam, 30 f/s 100 Gflop

0 0 0 1000 HDTV 60 20 Pflop

Progress:

Jan 1998: 10,000 x Realtime with a few SGIs (14 days elapse time)

Feb 1999: 1,000 x Realtime with 20 PCs (4 days elapse time)

Jan 2002: Modeling: < 50 x Realtime Display: Realtime

Page 33: Many-Camera Systems - CMU Panoptic Datasetdomedb.perception.cs.cmu.edu/tutorials/.../slides/invited1_Kanade.pdf · Many-Camera Systems: How They Started at CMU up to EyeVision at

camera

Visual Communication Lab

camera display

display

Cloned face

PC Cluster

Page 34: Many-Camera Systems - CMU Panoptic Datasetdomedb.perception.cs.cmu.edu/tutorials/.../slides/invited1_Kanade.pdf · Many-Camera Systems: How They Started at CMU up to EyeVision at

Many-Camera Systems

“Numerosity is power.”

Early efforts:

Primitives and Pretty good

Fun and useful

Challenges in devices, algorithms,

computation, communication

AND application scenarios