A Convenient Multicamera Self-Calibration for Virtual Environments

Virtual immersive environments or telepresence setups often consist of multiple cameras that have to be calibrated. We present a convenient method for doing this. The minimum is three cameras, but there is no upper limit. The method is fully automatic and a freely moving bright spot is the only calibration object. A set of virtual 3D points is made by waving the bright spot through the working volume. Its projections are found with subpixel precision and verified by a robust RANSAC analysis. The cameras do not have to see all points; only reasonable overlap between camera subgroups is necessary. Projective structures are computed via rank-4 factorization and the Euclidean stratification is done by imposing geometric constraints. This linear estimate initializes a postprocessing computation of nonlinear distortion, which is also fully automatic. We suggest a trick on how to use a very ordinary laser pointer as the calibration object. We show that it is possible to calibrate an immersive virtual environment with 16 cameras in less than 60 minutes reaching about 1/5 pixel reprojection error. The method has been successfully tested on numerous multicamera environments using varying numbers of cameras of varying quality.

[1]  K. S. Arun,et al.  Least-Squares Fitting of Two 3-D Point Sets , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Richard I. Hartley,et al.  Critical configurations for n-view projective reconstruction , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

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

[4]  Luc Van Gool,et al.  Blue-c: a spatially immersive display and 3D video portal for telepresence , 2003, IPT/EGVE.

[5]  Hideo Saito,et al.  Large-scale Virtualized Reality , 2001 .

[6]  Mohan M. Trivedi,et al.  Active Camera Networks and Semantic Event Databases for Intelligent Environments , 2002 .

[7]  Andrew W. Fitzgibbon,et al.  Bundle Adjustment - A Modern Synthesis , 1999, Workshop on Vision Algorithms.

[8]  Reinhard Koch,et al.  Self-Calibration and Metric Reconstruction Inspite of Varying and Unknown Intrinsic Camera Parameters , 1999, International Journal of Computer Vision.

[9]  Peter F. Sturm,et al.  A Case Against Kruppa's Equations for Camera Self-Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Peter F. Sturm,et al.  A Factorization Based Algorithm for Multi-Image Projective Structure and Motion , 1996, ECCV.

[11]  Stefano Soatto,et al.  A Variational Approach to Problems in Calibration of Multiple Cameras , 2007, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Sun Ji-zhou Creating 3D Models with Uncalibrated Cameras , 2001 .

[13]  Luc Van Gool,et al.  Monkeys -- A Software Architecture for ViRoom -- Low-Cost Multicamera System , 2003, ICVS.

[14]  Tomás Pajdla,et al.  Structure from Many Perspective Images with Occlusions , 2002, ECCV.

[15]  Bill Triggs,et al.  Critical Motions for Auto-Calibration When Some Intrinsic Parameters Can Vary , 2000, Journal of Mathematical Imaging and Vision.

[16]  Richard I. Hartley Ambiguous Configurations for 3-View Projective Reconstruction , 2000, ECCV.

[17]  Takeo Kanade,et al.  Shape-from-silhouette of articulated objects and its use for human body kinematics estimation and motion capture , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[18]  Takeo Kanade,et al.  Shape and motion from image streams under orthography: a factorization method , 1992, International Journal of Computer Vision.

[19]  Roger Y. Tsai,et al.  A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses , 1987, IEEE J. Robotics Autom..

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

[21]  Luc Van Gool,et al.  Markerless tracking of complex human motions from multiple views , 2006, Comput. Vis. Image Underst..

[22]  David W. Jacobs,et al.  Linear fitting with missing data: applications to structure-from-motion and to characterizing intensity images , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[23]  Luc Van Gool,et al.  ViRoom - Low Cost Synchronized Multicamera System and Its Self-calibration , 2002, DAGM-Symposium.

[24]  Barry Brumitt,et al.  EasyLiving: Technologies for Intelligent Environments , 2000, HUC.

[25]  Jean-Yves Bouguet,et al.  Camera calibration toolbox for matlab , 2001 .

[26]  Hirokazu Kato,et al.  3D live: real time captured content for mixed reality , 2002, Proceedings. International Symposium on Mixed and Augmented Reality.

[27]  Yiannis Aloimonos,et al.  Calibration of a Multicamera Network , 2003, 2003 Conference on Computer Vision and Pattern Recognition Workshop.

[28]  James W. Davis,et al.  The KidsRoom: A Perceptually-Based Interactive and Immersive Story Environment , 1999, Presence.

[29]  Rainer Lienhart,et al.  Calibrating and optimizing poses of visual sensors in distributed platforms , 2006, Multimedia Systems.

[30]  Lily Lee,et al.  Monitoring Activities from Multiple Video Streams: Establishing a Common Coordinate Frame , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[31]  Peter F. Sturm,et al.  Critical motion sequences for monocular self-calibration and uncalibrated Euclidean reconstruction , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[32]  PollefeysMarc,et al.  Self-Calibration and Metric Reconstruction Inspite of Varying and Unknown Intrinsic Camera Parameters , 1999 .

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

[34]  Tomás Pajdla,et al.  3D reconstruction by fitting low-rank matrices with missing data , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[35]  Andrew Zisserman,et al.  Multiple view geometry in computer visiond , 2001 .

[36]  Mubarak Shah,et al.  Human tracking in multiple cameras , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.