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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. [email protected]. - 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. [email protected]
Applied Imagery Pattern Recognition (AIPR) Workshop 2012Washington, DC
October 9-11, 2012
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Multi-View Reconstruction
Bundle Adjustment
‘dinosaur’ dataset images from [1].
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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
…
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Evaluated Test Cases
• Simulation test cases– Frame decimation simulation– Feature matching inaccuracy– Self calibration tests
• Comparison test cases
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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
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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
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
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Reprojection Error versus Angular Error
Reprojection Error Angular Error
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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.
