Dr. Muhammad Zeshan Afzal
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
Sk Aziz Ali
Mhd Rashed Al Koutayni
Murad Almadani
Alaa Alshubbak
Yuriy Anisimov
Jilliam Maria Diaz Barros
Ramy Battrawy
Iuliia Brishtel
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
Real-time Camera Pose Estimation using Correspondences with High Outlier Ratios – Solving the Perspective n-Point Problem using Prior Probability
Real-time Camera Pose Estimation using Correspondences with High Outlier Ratios – Solving the Perspective n-Point Problem using Prior Probability
VISAPP 2010 - Proceedings of the Fifth International Conference on Computer Vision Theory and Applications, Angers, France, May 17-21, 2010 - Volume 1 International Conference on Computer Vision Theory and Applications (VISAPP-2010), located at VISIGRAPP 2010, May 17-21, Angers, France
- Abstract:
- We present PPnP, an algorithm capable of estimating a robust camera pose in real-time, even if being provided with large sets of correspondences containing high ratios of outliers. For these situations, standard pose estimation algorithms using RANSAC are often unable to provide a solution or at least not in the required time frame. PPnP is provided with a probability distribution function which describes all valid possible camera pose estimates. By checking the correspondences for being compatible with the prior probability, it can be decided effectively at a very early stage, which correspondences can be treated as outliers. This allows a considerably more effective selection of hypothetical inliers than in RANSAC. Although PPnP is based on a technique called BlindPnP which is not intended for real-time computing, a number of changes in PPnP allows to estimate a camera pose with the same high quality as BlindPnP while being considerably faster.