improving p erformance and accuracy of local pca

PG 2011Pacific Graphics 2011

The

19th

Pac

ific

Conf

eren

ce o

n Co

mpu

ter G

raph

ics a

nd A

pplic

ation

s (P

acifi

c Gr

aphi

cs 2

011)

will

be

held

on

Sept

embe

r 21

to 2

3, 2

011

in K

aohs

iung

, Tai

wan

.

Improving Performance and Accuracy of Local PCA

V. Gassenbauer, J. Křivánek, K. Bouatouch, C. Bouville, M. Ribardière

University of Rennes 1, France, and Charles University, Czech Republic

Pacific Graphics 2011

2 | 25 Kaohsiung, Taiwan

• Need to compress large data set

ObjectivePrecomputed Radiance

Transfer (PRT)Bidirectional Texture Function

(BTF) compression

Image courtesy of Hongzhi Wu



• Relighting as a matrix-vector multiply

• Matrix T(x,ωi) - Billions of elements• Infeasible multiplication• Compression (wavelet, Local PCA)

Precomputed Radiance Transfer

NΜN2N1

3Μ3231

2Μ2221

1Μ1211

TTT

TTTTTTTTT

4

3

2

1

P

PPP

M

2

1

L

LL



• Precomputation-based rendering

Related Work

[Sloan et al. 02,03]Low-freq shadows,

inter-reflections

[Xu et al. 08]Dynamic scene,

BRDF editing

[Liu et al. 04], [Huang et al. 10]

High-freq shadows, inter-reflections

• Still use slow and inaccurate LPCA !



• BTF compression• [Müller et al. 03], [Filip et al. 09]

Related Work

• Simpler LPCA: k-means problem• Better performance

[Phillips 02], [Elkan 03], [Hamerly10]• Better accuracy

[Arthur et al. 07], [Kanugo et al. 02]



• Input• High-dimensional points

(rows of T)• Number of clusters k

Compression Problem

• Output• Find k clusters – i.e.low-dimensional subspaces

x13x14

x104

x960



• Guess k clusters (i.e. subs.)• Initialize by randomly selected

centers

LPCA – The Algorithm

• Assign each point to the nearest cluster

• Update PCA in the cluster

• Repeat until convergence

Generate seeds

Classification

Update

Until converge



Motivation

• Inaccurate

• Inefficient• For each point compute distance

to all clusters ai

• Prone to get stuck in a local optimum

x

a1 a2

ak

Generate seeds

Classification

Update

Until converge



Our Contribution

• Inaccurate

• Inefficient• For each point compute distance

to all clusters ai

• Prone to get stuck in a local optimum

• SortMeans++

x

a1 a2

ak

• SortCluster LPCA (SC-LPCA)Generate seeds

Classification

Update

Until converge



• Improving Performance• SortCluster-LPCA (SC-LPCA)

• Improving Accuracy• SortMeans++

• Results

Outline



If d(ca,cb) ≥ 2d(x,ca)

• ∆-inequality [Phillips 02]

Accelerated k-means

x

d(ca,cb)

d(x,cb )

d(x,

c a)

cb

ca

→ d(x,cb) ≥ d(x,ca)

→ d(x,cb) not necessary

to compute !



How does it work ?We know: potentially nearest cluster to xWe know: Distances of other cluster w.r.t. ca

Check d(ca,cb) ≥ 2d(x,ca)Compute d(x,ca)Compute d(x,cb)

ca

cb cc

x

Check d(ca,cc) ≥ d(x,ca)+d(x,cb) cb becomes a new nearest cluster



Our Contribution: From k-means to LPCA

x

cacb

d(ca,cb)

d(x,cb )

d(x,

c b)

xd(x,ab)d(x,a

b )

d(aa,ab)

ab

dir(a b

)

aa

dir(

a a)

k-means LPCAPiecewise

reconstructionPiecewise reconstruction inlow-dimensional subspaces



• Distance between subspaces• d(aa,ab) = inf{║p - q║; p in aa, q in ab }

∆-inequality for LPCA

aaab

xd(x,ab) d(x,a

b )

d(aa,ab)

• ∆-inequality for subspaces

If d(aa,ab) ≥ 2d(x,ab)

→ d(x,ab) ≥ d(x,ab)

→ d(x,ab) not necessary to

compute !

dir(a b

)

jarda

zjednodus znaceni: A misto a_a, B misto a_b atd ... je potreba maximalne zvysit citelnost



Our SortCluster-LPCA

• When assigning x• Start from aj

• Proceed ai in increasing order of distances w.r.t. aj

• Check ∆-inequality

• For all ai precompute• Distances to each others• Ordering

Precompute distances

Generate seeds

Classification

Update

Until converge

using ∆-ineq.





• Results

Outline



• Observation• Error comes from

poor selections of cluster centers

LPCA Accuracy

• LPCA prone to stuck in local optimum



• Some heuristic• Farthest first [Hochbaum et al. 85] • Sum based [Hašan et al. 06]• k-means++ [Arthur and Vassil. 07]• …

• Our approach: SortMeans++• Based on k-means++• Faster

Generation of Seeds

Generate seeds

Classification

Update

Until converge


using ∆-ineq.



• Select initial seeds

Our SortMeans++

Select 1st seed at randomTake 2nd seed with probability equal distancesRecluster using ∆-inequalityTake 3rd seed with probability equal distancesRecluster …





• Results

Outline



• Evaluate method with different parameters• Scalability with the subspace dimension

Results

• More than 5x speed-up



• Did several iterations of (SC)LPCA up to 24 basis

Overall Performance

Model Vertices [#]

LPCA SC-LPCA Speed Up PRT

Horse 67.6k 3h 48m 11m 20.3x 22.5 sDragon 57.5k 3h 1m 28m 6.4x 25.5 sBuddha 85.2k 4h 38m 50m 5.6x 31.6 sDisney 106.3k 5h 50m 1h 8m 5.1x 46.5 s



• Latency and accuracy of seeding algs:

Improving Accuracy

• Our SortMeans++• Generally lowest error & fast• Improve performance of SC-LPCA !



• SC-LPCA (accelerated LPCA)• Avoid unnecessary distance comp.• Speed-up of 5 to 20 on our PRT data• Without changing output !

Conclusion

• Improve accuracy (SortMeans++)• More accurate data approximation

• Future work• Test on other CG data• GPU acceleration

Generate seeds

Classification

Update

Until converge


using ∆-ineq.

SortMeans++



• SC-LPCA speed-up of about only 1.5x• Reason: Small number of subspaces

BTF Compression

• Try several data sets



• Acknowledgement• European Community

• Marie Curie Fellowship PIOF-GA-2008-221716

• Ministry of Education, Czech Republic• Research program LC-06008.

• Anonymous reviewers

The End

Thank You for your attention

improving p erformance and accuracy of local pca

Documents