visualization of scene structure uncertainty in a multi-view reconstruction pipeline shawn recker 1,...

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Visualization of Scene Structure Uncertainty in a Multi-View Reconstruction Pipeline

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

Kenneth I. Joy1

1University of California, Davis, USA, {strecker, mhessf, joy}@ucdavis.edu2 Lawrence Livermore National Labs. duchaineau@cognigraph.com

Vision, Modeling, and Visualization (VMV) Workshop 2012Magdeburg, Germany12 -14 November 2012

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Multi-View Reconstruction

Bundle Adjustment

‘dinosaur’ dataset images from [1].

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Structural Uncertainty Visualization

Volume Visualization Techniques

0 1 2

321

2 3 41 2 3

432

3 4 52 3 4

543

4 5 6

Volume Rendering

Contouring

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Procedure

…

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Evaluated Test Cases

• Frame decimation simulation• Feature matching inaccuracy• Self calibration tests

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Frame Decimation Graphs

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Frame Decimation Results

30 cameras 15 cameras 10 cameras

8 cameras 4 cameras 2 cameras

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Feature Tracking Graphs

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Feature Tracking Inaccuracy Results

0% Error 1% Error 2% Error

5% Error 10% Error 20% Error

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Self-Calibration Graphs

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Self-Calibration Results

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

Principal Point Variation

Focal Length Decrease

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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

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Acknowledgements

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

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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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Reprojection Error versus Angular Error

Reprojection Error Scalar Field

Average Scalar Field