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Compression, Processing, Indexing and Retrieval of 3D Objects and Data:

How to extend image/video processing to graphics?

Tsuhan ChenCarnegie Mellon University

tsuhan@cmu.edu

Joint work with Howard Leung, Masa Okuda, and Cha Zhang

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(Mis)UnderstandingTo graphics and vision communities

Video is just low-level processingTo the video community

Graphics is just some fancy toolsVision is things that don’t work in practice

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First Attempt…MPEG-4

Started out as model-based codingAnalysis and synthesisUsing vision/graphics for video coding

That didn’t happen (not completely)Settled with 2D shape-based codingModel-based coding for limited content, e.g., faces

Modeling and CodingEXAMPLESMODELS CODED INFORMATION

PCM

Predictive CodingTransform Coding

Block-based codingH.261/263, MPEG-1/2

Model-based coding

MPEG-4

Pixels

Statistically dependent pixels

Moving blocks

Moving objects

Facial models

Moving regions

Color of pixels

Prediction error ortransform coeffs

Motion vectors and prediction error

Shapes, motion, and colors of objects

Action units

Shapes, motion, and colors of regions

Region-based codingH.263+, MPEG-4

MPEG-7A/V objects Description

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Modeling and Coding (cont.)Better modeling implies

Higher compressionMore content accessibilityMore complexityLess error resilience

Video and vision/graphics do go hand-in-hand all alongVideo research is evolution of vision and graphics techniques

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TopicsCompression for image based renderingCompression for 3D meshes

Streaming in texture and geometry jointly Indexing and retrieval of 3D objectsBuilding immersive environments

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Image-Based Rendering

……

[Shum et. al]

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CompressionThe number of images is large, so we need compressionGood to have fewer samples

Does not guarantee fewer bits

Consider these as a video sequenceGeneral video coding applies

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DCT Q

IDCT

IQ

D

ME

MCMV

?

?+

+IDCT

IQ

+D MC

MV

Network or Storage

Encoder Decoder

Video Codec

1 Intra coding2 Inter coding

1

1 1

2

2 2

Previous frame(reference frame)

Current frame

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Intra Coding

Disadvantage: Does not exploit the correlation between images

I-frame I-frame

……

1 2 3I-frame

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Inter Coding

Disadvantage: Does not provide random access

i.e., frame N depends on frame N-1

I-frame P-frame

……

1 2 3P-frame

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Prediction from Sprite

25…………

1 2 k3

[cf. Anandan et. al]

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Generation of SpriteImage 1

Image 2

Image N-1

Image N

Image 1

Image 1

Image 2

Image N-1

Image N

Sprite

Step 1: Finding the offset Step 2: Generating the sprite

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Weighting- need to find a weighting function to blend the

images to form the sprite

0

0.001

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Column number

Wei

ght

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Column number

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ght

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Column number

Wei

ght

Constant weighting Triangular weighting Delta weighting

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Constant Weighting

…………

1 2 k3

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Triangular Weighting

…………

1 2 k3

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Delta Weighting

…………

1 2 k3

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Modified Codec3 Prediction from sprite image without MC

DCT Q

Prediction

IQ?

?+ IDCT

Network or Storage

Encoder Decoder

sprite + offset

Prediction

+

sprite + offset

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With Motion Compensation4 Prediction from sprite image with MC

DCT Q

Prediction

IQ

ME

MCMV

?

?+ IDCT

Network or Storage

Encoder Decoder

Sprite + offset

Prediction

MCMV

Sprite + offset

+

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With vs. Without MC

3 4without MC with MC

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Test Sequences (1)

Synthetic sequence 1: NetICE room Synthetic sequence 2: Park

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Test Sequences (2)

Real sequence 1: Kids Real sequence 2: Kongmiao

[Shum, et al]

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Weighting function Results

Synthetic sequence 1 Synthetic sequence 2

Real sequence 1 Real sequence 2

25

27

29

31

33

35

37

0 0.05 0.1 0.15 0.2

Bit rate (bpp)

PSNR

(dB)

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37

0 0.05 0.1 0.15 0.2

Bit rate (bpp)

PSNR

(dB)

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0 0.02 0.04 0.06 0.08 0.1

Bit rate (bpp)

PSNR

(dB)

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44

0 0.01 0.02 0.03 0.04 0.05

Bit rate (bpp)

PSNR

(dB)

Constant weighting Triangular weighting Delta weighting

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Compression Result

Synthetic sequence 1 Synthetic sequence 2

29

31

33

35

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39

41

0 0.05

Bit rate (bpp)

PS

NR

(dB

)

Intra coding Inter coding Mosais without MC Mosaic with MC

34

36

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40

42

44

0 0.01 0.02 0.03 0.04 0.05

Bit rate (bpp)P

SN

R (d

B)

Intra coding Inter coding Mosais without MC Mosaic with MC

Intra coding Inter coding Sprite without MC Sprite with MC

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Compression Result

Real sequence 1 Real sequence 2

2526272829303132333435

0 0.05 0.1

Bit rate (bpp)

PS

NR

(dB

)

Intra coding Inter coding Mosais without MC Mosaic with MC

25262728293031323334

0 0.05 0.1

Bit rate (bpp)P

SN

R (d

B)

Intra coding Inter coding Mosais without MC Mosaic with MC

Intra coding Inter coding Sprite without MC Sprite with MC

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EnhancementsWindow size for searching offsetsStripe motion compensationMC using a large reference frameMultiple sprites

Kids

26

27

28

29

30

31

32

33

0.2 0.3 0.4 0.5 0.6 0.7 0.8

bit rate (bpp)

PS

NR

(d

B)

1 sprite 3 sprites 5 sprites 7 sprites 9 sprites

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Recap…Sprite prediction with MC better than Intra coding

Sprite prediction with MC is preferred for random accessBetter than Inter coding for real data

Delta weighting is the best for constructing the spriteCan be extended to higher dimensions

Lumigraph, lightfield, etc.

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Streaming 3D

3 second

10 seconds40 seconds

Geometry/Texture

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Texture + Geometry = 3D Object

Corner-Based

Vertex-Based

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Why Compression?Each vertex: three floating-point numbersIf each vertex shared by 6 triangles, and max number of vertices per model is 220

? 108 bits/triangle needed

? 100KB~1MB for an average model + texture

trianglebits

IDvertexbits

triangleIDsvertex

vertexbits

trianglevertices 10820

*33*32

*3

*61 ????

?????

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Compression of 3D ObjectsTexture compression

Static textures: JPEG or JPEG 2000Dynamic textures: MPEG or H.263

Geometry compressionQuantization of vertex coordinatesPredictive codingEntropy coding

Granular/stable progressive codingMesh optimization/simplification

[Hoppe et al][Heckbert et al][Schroder et al][Taubin et al.]

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Texture Coding

Block Diagram

Vertex Quantization

Entropy Coding

Vertex Coordinates

Prediction

Bitstream

Connectivity++

-

3D ModelTexture

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EncodingVertex decimation

C

C

1

234

56

165

4

Re-triangulation

? ?iv

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Importance of Vertices

(1) Volume

(2) Color

)(iv

)(ic

V1V2

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Rank all vertices from high to low based on a cost function:

v (i ) is the geometry costc (i ) is the texture/color/normal cost? is an user-specified parameter

Decimate the vertices with low cost firstTransmit the vertices with high cost first

)()1()()( icivim ?? ???

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Coding of TextureVertex-based

Wavelet (SPIHT) + entropy coding

Corner-basedPadding + DCT + run-length coding + entropy codingTexture re-mapping needed

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Texture Re-Mapping

m

v

vm

(a) (b)

New Texture M ap

t’1

t’2 t’3

t’5

t’4

t1 t2 t3 t4 t5

Texture Rem apping

Retriangulate

t6

t’6

(c)

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683651605

79266234

6750

VRML+ gzip

texture-126Vasetexture-15Ducknone1814Pieta

texture-160Totem

none4840Horsenone4136CrocodileNone1512CowNone1410Beethoven

AttributesMPEG-4OurAlgorithm

(in KBytes)

Comparison

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View-Adaptive Transmission

Viewpoint B

Viewpoint A

Hypothetical Viewpoint

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Retrieval of 3D ObjectsIndexing and retrieval

Much is done for images[Huang et al][Cox et al]

Recent work for 3D objectsRelated to MPEG-7

Feature extractionFeature matching

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Feature ExtractionFeature extraction

Traditionally vertex/surface-basedNew region-based features

moment invariants, Fourier transform coefficients, etc.

Preprocessing to close the model

Surface Region

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Feature Extraction (cont.)Efficiently calculate region-based feature directly from meshSigned feature for each mesh elementRobust to triangulationApplies to any feature that can be decomposed to each mesh element +

??

?+

+

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3D Model Retrieval

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Annotation and Active LearningSemantic thru annotation is needed

Low level features not enoughHierarchical annotationCompatible concepts in annotation

Active learningComplete annotation is impracticalSelect the object most uncertain forannnoation

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Annotation

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Active LearningFor each model, each concept, we maintain a probability of this model belonging to this conceptSet the probability to 1 or 0 if annotatedEstimate probabilities of the unlabeled objects with potential functionUse the probabilities to estimate uncertainty and to measure the semantic distance

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Active Learning

-5 -4 -3 -2 -1 0 1 2 3 4 50.5

0.55

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1

max2 d?

d

p )/exp(5.05.0 2max

20 ddcp ????

-10 -8 -6 -4 -2 0 2 4 6 8 100.5

0.55

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1

f

kp

Annotated models

One annotated neighborhood Multiple annotated neighborhoods

The potential function

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Estimate the UncertaintyThe general criterion:

fDi: local density functionUi: uncertainty measurement

. ..., ,2 ,1 ),,...,,( 21 NipppfUfG iKiiiDiiDi ??????

).1log()1(log),...,,( maxmaxmaxmax21 iiiiiKiii pppppppU ???????

Kkpp ikki ..., ,2 ,1 ),(maxmax ??

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Estimate the Uncertainty

1p

2p

iep

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Semantic DistanceCannot use Kullback-Leibler convergenceOur semantic disance is defined as:

Annotated models

Unannotated modes

? ?.

,eeconcept tr in the levelat is concept ,1:1maxlLowestLeve

s

jkikk

d

lkandppllLowestLeve

??

????

? ?

? ?

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. 0 if ,)1(

0 if ,min1 ;eeconcept tr in the levelat is concept :1

;eeconcept tr in the levelat is concept

,5.0 5.0 ,:maxarg

;,...,2,1 ),1()1(

0

0

20

lLowestLeved

lLowestLevedd

lkllLowestLevelkand

porpTdlk

Kkppppd

lLowestLevesk

skks

jkiksk

k

ikjkjkiksk

?

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Results

0

0.5

1

1.5

2

2.5

3

0 50 100 150 200 250 300 350

# of Samples Annotated

Ret

riev

al P

erfo

rman

ce (D

)

Best Gradient Search

Random Sampling

Our Algorithm

0

1

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5

6

0 50 100 150 200 250

# of Models Annotated

Ret

riev

al P

erfo

rman

ce (D

) Best Gradient Search

Random Sampling

Our Algorithm

Synthetic database A small database

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Results (cont.)

0

1

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8

9

0 500 1000 1500 2000

# of models annotated

Ret

riev

al P

erfo

rman

ce (D

) Random Sampling

Our algorithm

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Recap…New feature set for 3D modelsActive learning to improve annotation efficiencyCompatible concept tree for annotationProbability for both uncertainty estimation and semantic distance

“Collaboration from anywhere, through any media, as if face-to-face in one room”

Network

Immersive Environments

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A PrototypeNetICE: Networked Intelligent Collaborative EnvironmentLip-sync facilitates speech understanding

Who is speaking and what is being saidConsistent spatial relationship with eye contact

Whom is spoken toFacial expressions and voice-driven hand gesturesDirectional sound give sense of distance and direction

Who is where; Who is speakingEnable small-group interaction in a room full of people

Information sharingShared whiteboardStreaming 3D objectsEnable collaborative design, e.g., cars, buildings, etc.

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NetICE

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NetICE

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NetICE

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NetICE

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Case Study: Online Auction

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Ongoing WorkUse IBR for background renderingUser study

Together or on-locationTracking for rendering

Head tracking for head orientationGaze tracking for eye contactHand tracking for hand gestures

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SummaryCompression for IBRCompression for 3D meshesIndexing and retrieval of 3D objectsImmersive environments

Tsuhan Chen

Advanced Multimedia Processing Lab

Please visit us at:http://amp.ece.cmu.edu