Markerless Camera Pose Estimation - An Overview

Human perception shows that a correct interpretation of a 3D scene on the basis of a 2D image is possible without markers. Solely by identifying natural features of dierent objects, their locations and orientations on the image can be identified. This allows a three dimensional interpretation of a two dimensional pictured scene. The key aspect for this interpretation is the correct estimation of the camera pose, i.e. the knowledge of the orientation and location a picture was recorded. This paper is intended to provide an overview of the usual camera pose estimation pipeline as well as to present and discuss the several classes of pose estimation algorithms.

[1]  J. Challis A procedure for determining rigid body transformation parameters. , 1995, Journal of biomechanics.

[2]  Michel Dhome,et al.  Determination of the Attitude of 3D Objects from a Single Perspective View , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Jianliang Tang,et al.  Complete Solution Classification for the Perspective-Three-Point Problem , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Matthijs C. Dorst Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[6]  V. Lepetit,et al.  EPnP: An Accurate O(n) Solution to the PnP Problem , 2009, International Journal of Computer Vision.

[7]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[8]  Long Quan,et al.  Linear N-Point Camera Pose Determination , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Robert M. Haralick,et al.  Review and analysis of solutions of the three point perspective pose estimation problem , 1994, International Journal of Computer Vision.

[10]  QuanLong,et al.  Linear N-Point Camera Pose Determination , 1999 .

[11]  K. Satoh,et al.  A hybrid and linear registration method utilizing inclination constraint , 2005, Fourth IEEE and ACM International Symposium on Mixed and Augmented Reality (ISMAR'05).

[12]  Larry S. Davis,et al.  Model-based object pose in 25 lines of code , 1992, International Journal of Computer Vision.

[13]  Vincent Lepetit,et al.  Randomized trees for real-time keypoint recognition , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[14]  Didier Stricker,et al.  Real-time Camera Pose Estimation using Correspondences with High Outlier Ratios - Solving the Perspective n-Point Problem using Prior Probability , 2010, VISAPP.

[15]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[16]  Vincent Lepetit,et al.  Pose Priors for Simultaneously Solving Alignment and Correspondence , 2008, ECCV.