visualization of scene structure uncertainty in multi-view reconstruction

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Visualization of Scene Structure Uncertainty in Multi-View Reconstruction. Shawn Recker 1 , Mauricio Hess-Flores 1 , Mark A. Duchaineau 2 , and Kenneth I. Joy 1. 1 University of California, Davis, USA, { strecker , mhessf , joy}@ucdavis.edu 2 Google, Inc. duchaineau@google.com. - PowerPoint PPT Presentation

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1

Visualization of Scene Structure Uncertainty in Multi-View Reconstruction

Shawn Recker1, Mauricio Hess-Flores1, Mark A. Duchaineau2, and

Kenneth I. Joy1

1University of California, Davis, USA, {strecker, mhessf, joy}@ucdavis.edu2Google, Inc. duchaineau@google.com

Applied Imagery Pattern Recognition (AIPR) Workshop 2012Washington, DC

October 9-11, 2012

2

Multi-View Reconstruction

Bundle Adjustment

‘dinosaur’ dataset images from [1].

3

Structural Uncertainty Visualization

Volume Visualization

0 1 2

321

2 3 41 2 3

432

3 4 52 3 4

543

4 5 6

Volume Rendering

Contouring

4

5

Procedure

6

Evaluated Test Cases

• Simulation test cases– Frame decimation simulation– Feature matching inaccuracy– Self calibration tests

• Comparison test cases

7

Frame Decimation Graphs

30 15 10 8 4 20

0.05

0.1

0.15

0.2

0.25

0.3

0.35

Average Value vs Number of Cameras

CircleSemiLineRandom

Number of Cameras

Aver

age

Valu

e

30 15 10 8 4 20

500

1000

1500

2000

2500

Isosurface Volume vs Number of Cameras

CircleSemiLineRandom

Number of Cameras

Isosu

rfac

e Vo

lum

e

8

Frame Decimation Results

30 cameras 15 cameras 10 cameras

8 cameras 4 cameras 2 cameras

9

Feature Tracking Graphs

0% 1% 2% 5% 10% 20%0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

Average Value vs Feature Tracking Error

CircleSemiLineRandom

Random Feature Tracking Error

Aver

age

Valu

e

0% 1% 2% 5% 10% 20%0

100

200

300

400

500

600

700

800

900

1000

Isosurface Volume vs Feature Tracking Error

CircleSemiLineRandom

Number of Cameras

Isosu

rfac

e Vo

lum

e

10

Feature Tracking Inaccuracy Results

0% Error 1% Error 2% Error

5% Error 10% Error 20% Error

11

Reprojection Error versus Angular Error

Reprojection Error Angular Error

12

Conclusions and Future Work

• Presentation of a structural uncertainty visualization tool

• Continued visualization of computer vision• Investigation of our cost function– Scene structure computation– Camera pose estimation

13

Acknowledgements

• This work was supported in part by Lawrence Livermore National Laboratory and the National Nuclear Security Agency through Contract No. DE-FG52-09NA29355

14

References

[1] Oxford Visual Geometry Group, “Multi-view and Oxford Colleges building reconstruction,” August 2009.[2] V. Rodehorst, M. Heinrichs, and O. Hellwich, “Evaluation of relative pose estimation methods for multi-camera setups,” in International Archives of Photogrammetry and Remote Sensing (ISPRS ’08), (Beijing, China), pp. 135–140, 2008.[3] D. Knoblauch, M. Hess-Flores, M. A. Duchaineau, and F. Kuester, “Factorization of correspondence and camera error for unconstrained dense correspondence applications,” in 5th International Symposium on Visual Computing, pp. 720–729, 2009.[4] T. Torsney-Weir, A. Saad, T. M´’oller, H.-C. Hege, B. Weber, and J.-M. Verbavatz, “Tuner: Principled parameter finding for image segmentation algorithms using visual response surface exploration,” IEEE Trans. On Visualization and Computer Graphics, vol. 17, no. 12, pp. 1892–1901, 2011.[5] A. Saad, T. M´’oller, and G. Hamarneh, “Probexplorer: Uncertainty guided exploration and editing of probabilistic medical image segmentation,” Computer Graphics Forum, vol. 29, no. 3, pp. 1113–1122, 2010.

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