Consistent Pose Uncertainty Estimation for Spherical Cameras
Bernd Krolla, Christiano Couto Gava, Alain Pagani, Didier Stricker
International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG-2014), 22nd, June 2-5, Pilzen, Czech Republic

Abstract:

In this work, we discuss and evaluate the reliability of first order uncertainty propagation results in context of spherical Structure from Motion, concluding that they are not valid without restrictions, but depend on the choice of the objective function. We furthermore show that the choice of the widely used geodesic error as objective function for a reprojection error optimization leads to disproportional pose uncertainty results of spherical cameras. This work identifies and outlines alternative objective functions to bypass those obstacles by deducing Jacobian matrices according to the chosen objective functions with subsequent conduction of first order uncertainty propagation. We evaluate the performance of the different objective functions in different optimization scenarios and show that best results for uncertainty propagation are obtained using the Euclidean distance to measure deviations of image points on the spherical image.

Keywords:

computer vision, 3D reconstruction, spherical imaging, uncertainty propagation, error propagation, camera pose optimization, spherical SfM

Consistent Pose Uncertainty Estimation for Spherical Cameras
Vaclav Skala (Hrsg.)
Communication Papers Proceedings of the 22nd International Conference on Computer Graphics, Visualization and Computer Vision (WSCG) International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG-2014), 22nd, June 2-5, Pilzen, Czech Republic

Abstract:

In this work, we discuss and evaluate the reliability of first order uncertainty propagation results in context of spherical Structure from Motion, concluding that they are not valid without restrictions, but depend on the choice of the objective function. We furthermore show that the choice of the widely used geodesic error as objective function for a reprojection error optimization leads to disproportional pose uncertainty results of spherical cameras. This work identifies and outlines alternative objective functions to bypass those obstacles by deducing Jacobian matrices according to the chosen objective functions with subsequent conduction of first order uncertainty propagation. We evaluate the performance of the different objective functions in different optimization scenarios and show that best results for uncertainty propagation are obtained using the Euclidean distance to measure deviations of image points on the spherical image.

Keywords:

computer vision, 3D reconstruction, spherical imaging, uncertainty propagation, error propagation, camera pose optimization, spherical SfM