Prof. Dr. Didier Stricker
Dr. Alain Pagani
Dr. Gerd Reis
Eric Thil
Keonna Cunningham
Dr. Oliver Wasenmüller
Dr. Gabriele Bleser
Dr. Bruno Mirbach
Dr. Jason Raphael Rambach
Dr. Bertram Taetz
Dr. Muhammad Zeshan Afzal
Sk Aziz Ali
Mhd Rashed Al Koutayni
Murad Almadani
Alaa Alshubbak
Yuriy Anisimov
Jilliam Maria Diaz Barros
Ramy Battrawy
Hammad Butt
Mahdi Chamseddine
Steve Dias da Cruz
Fangwen Shu
Torben Fetzer
Ahmet Firintepe
Sophie Folawiyo
David Michael Fürst
Kamalveerkaur Garewal
Christiano Couto Gava
Leif Eric Goebel
Tewodros Amberbir Habtegebrial
Simon Häring
Khurram Hashmi
Jigyasa Singh Katrolia
Andreas Kölsch
Onorina Kovalenko
Stephan Krauß
Paul Lesur
Muhammad Jameel Nawaz Malik
Michael Lorenz
Markus Miezal
Mina Ameli
Nareg Minaskan Karabid
Mohammad Minouei
Pramod Murthy
Mathias Musahl
Peter Neigel
Manthan Pancholi
Qinzhuan Qian
Engr. Kumail Raza
Dr. Nadia Robertini
María Alejandra Sánchez Marín
Dr. Kripasindhu Sarkar
Alexander Schäfer
Pascal Schneider
René Schuster
Mohamed Selim
Lukas Stefan Staecker
Dennis Stumpf
Yongzhi Su
Xiaoying Tan
Yaxu Xie
Dr. Vladislav Golyanik
Dr. Aditya Tewari
André Luiz Brandão
Consistent Pose Uncertainty Estimation for Spherical Cameras
Consistent Pose Uncertainty Estimation for Spherical Cameras
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