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Visualization of Scene Structure Uncertainty in Multi-View Reconstruction

This paper presents a tool for visualizing structural uncertainty in multi-view reconstruction. By utilizing images from a 'dinosaur' dataset, the authors evaluate various simulation test cases, including frame decimation and feature matching inaccuracies. They conduct detailed analyses on reprojection errors, angular errors, and feature tracking inaccuracies with varying camera counts. The findings aim to advance techniques in scene structure computation and camera pose estimation, contributing to the field of computer vision and multi-view reconstruction methodologies.

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Visualization of Scene Structure Uncertainty in Multi-View Reconstruction

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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.edu 2Google, Inc. duchaineau@google.com Applied Imagery Pattern Recognition (AIPR) Workshop 2012 Washington, DC October 9-11, 2012

  2. Multi-View Reconstruction Bundle Adjustment ‘dinosaur’ dataset images from [1].

  3. Structural Uncertainty Visualization

  4. Volume Visualization 5 6 4 Volume Rendering 5 3 4 2 3 4 4 5 3 4 2 3 1 2 3 3 4 2 Contouring 3 1 2 1 2 0

  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

  8. Frame Decimation Results 10 cameras 15 cameras 30 cameras 8 cameras 4 cameras 2 cameras

  9. Feature Tracking Graphs

  10. Feature Tracking Inaccuracy Results 1% Error 0% 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: Principledparameterfindingforimagesegmentationalgorithmsusing visual response surfaceexploration,” IEEE Trans. OnVisualizationand ComputerGraphics, vol. 17, no. 12, pp. 1892–1901, 2011. [5] A. Saad, T. M´’oller, and G. Hamarneh, “Probexplorer: Uncertaintyguidedexplorationand editing of probabilistic medical imagesegmentation,” Computer Graphics Forum, vol. 29, no. 3, pp. 1113–1122, 2010.

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