Calibration of Nodal and Free-Moving Cameras in Dynamic Scenes for Post-Production

In film production, many post-production tasks require the availability of accurate camera calibration information. This paper presents an algorithm for through-the-lens calibration of a moving camera for a common scenario in film production and broadcasting: The camera views a dynamic scene, which is also viewed by a set of static cameras with known calibration. The proposed method involves the construction of a sparse scene model from the static cameras, with respect to which the moving camera is registered, by applying the appropriate perspective-n-point (PnP) solver. In addition to the general motion case, the algorithm can handle the nodal cameras with unknown focal length via a novel P2P algorithm. The approach can identify a subset of static cameras that are more likely to generate a high number of scene-image correspondences, and can robustly deal with dynamic scenes. Our target applications include dense 3D reconstruction, stereoscopic 3D rendering and 3D scene augmentation, through which the success of the algorithm is demonstrated experimentally.

[1]  Martin Byröd,et al.  Pose estimation with radial distortion and unknown focal length , 2009, CVPR.

[2]  Nikos Paragios,et al.  Handbook of Mathematical Models in Computer Vision , 2005 .

[3]  Jiri Matas,et al.  Optimal Randomized RANSAC , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Hauke Strasdat,et al.  Real-time monocular SLAM: Why filter? , 2010, 2010 IEEE International Conference on Robotics and Automation.

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

[6]  Jean-Yves Guillemaut,et al.  Moving Camera Registration for Multiple Camera Setups in Dynamic Scenes , 2010, BMVC.

[7]  Jeffrey K. Uhlmann,et al.  Unscented filtering and nonlinear estimation , 2004, Proceedings of the IEEE.

[8]  F. Markley Attitude Error Representations for Kalman Filtering , 2003 .

[9]  Jiri Matas,et al.  Matching with PROSAC - progressive sample consensus , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[10]  A. Laurentini,et al.  The Visual Hull Concept for Silhouette-Based Image Understanding , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Michael M. Kazhdan,et al.  Poisson surface reconstruction , 2006, SGP '06.

[12]  Richard Szeliski,et al.  Modeling the World from Internet Photo Collections , 2008, International Journal of Computer Vision.

[13]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[14]  Adrian Hilton,et al.  Wand-based Multiple Camera Studio Calibration , 2007 .

[15]  Jean-Yves Guillemaut,et al.  Robust graph-cut scene segmentation and reconstruction for free-viewpoint video of complex dynamic scenes , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[16]  Olivier Stasse,et al.  MonoSLAM: Real-Time Single Camera SLAM , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[18]  G. Klein,et al.  Parallel Tracking and Mapping for Small AR Workspaces , 2007, 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality.

[19]  Harry Shum,et al.  Background Cut , 2006, ECCV.

[20]  Adrien Bartoli,et al.  Efficient Camera Smoothing in Sequential Structure-from-Motion Using Approximate Cross-Validation , 2008, ECCV.

[21]  Cordelia Schmid,et al.  A Performance Evaluation of Local Descriptors , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Y. Oshman,et al.  Averaging Quaternions , 2007 .

[23]  Kenichi Kanatani,et al.  Analysis of 3-D Rotation Fitting , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

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

[25]  Cordelia Schmid,et al.  A Comparison of Affine Region Detectors , 2005, International Journal of Computer Vision.

[26]  Hans-Peter Seidel,et al.  Markerless Motion Capture with unsynchronized moving cameras , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.