Plate refractive camera model and its applications

Abstract. In real applications, a pinhole camera capturing objects through a planar parallel transparent plate is frequently employed. Due to the refractive effects of the plate, such an imaging system does not comply with the conventional pinhole camera model. Although the system is ubiquitous, it has not been thoroughly studied. This paper aims at presenting a simple virtual camera model, called a plate refractive camera model, which has a form similar to a pinhole camera model and can efficiently model refractions through a plate. The key idea is to employ a pixel-wise viewpoint concept to encode the refraction effects into a pixel-wise pinhole camera model. The proposed camera model realizes an efficient forward projection computation method and has some advantages in applications. First, the model can help to compute the caustic surface to represent the changes of the camera viewpoints. Second, the model has strengths in analyzing and rectifying the image caustic distortion caused by the plate refraction effects. Third, the model can be used to calibrate the camera’s intrinsic parameters without removing the plate. Last but not least, the model contributes to putting forward the plate refractive triangulation methods in order to solve the plate refractive triangulation problem easily in multiviews. We verify our theory in both synthetic and real experiments.

[1]  Atsushi Yamashita,et al.  Three dimensional measurement of objects in liquid and estimation of refractive index of liquid by using images of water surface with a stereo vision system , 2008, 2008 IEEE International Conference on Robotics and Automation.

[2]  Yuncai Liu,et al.  Camera Calibration for Plate Refractive Imaging System , 2014, 2014 22nd International Conference on Pattern Recognition.

[3]  João Paulo Costeira,et al.  Stereo Reconstruction of a Submerged Scene , 2005, IbPRIA.

[4]  Peter F. Sturm,et al.  Towards complete generic camera calibration , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[5]  Richard I. Hartley,et al.  Multiple-View Geometry Under the {$L_\infty$}-Norm , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Young-Hoo Kwon,et al.  Effects of light refraction on the accuracy of camera calibration and reconstruction in underwater motion analysis , 2006, Sports biomechanics.

[7]  Shree K. Nayar,et al.  Non-Single Viewpoint Catadioptric Cameras: Geometry and Analysis , 2006, International Journal of Computer Vision.

[8]  Alexandru Tupan,et al.  Triangulation , 1997, Comput. Vis. Image Underst..

[9]  J. Casebolt,et al.  Effects of light refraction on the accuracy of camera calibration and reconstruction in underwater motion analysis , 2006, Sports biomechanics.

[10]  Xu Zhao,et al.  Underwater camera model and its use in calibration , 2015, 2015 IEEE International Conference on Information and Automation.

[11]  S. L. Shmakov A UNIVERSAL METHOD OF SOLVING QUARTIC EQUATIONS , 2011 .

[12]  Shree K. Nayar,et al.  A general imaging model and a method for finding its parameters , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[13]  Rajiv Gupta,et al.  Stereo from uncalibrated cameras , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[14]  Wei Liu,et al.  Calibration of central catadioptric camera with one-dimensional object undertaking general motions , 2011, 2011 18th IEEE International Conference on Image Processing.

[15]  Sagi Filin,et al.  Photogrammetric modeling of underwater environments , 2010 .

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

[17]  Minglun Gong,et al.  Refractive Epipolar Geometry for Underwater Stereo Matching , 2011, 2011 Canadian Conference on Computer and Robot Vision.

[18]  B. Zhang,et al.  A method for calibrating the central catadioptric camera via Homographic matrix , 2008, 2008 International Conference on Information and Automation.

[19]  Xianghua Ying,et al.  Catadioptric camera calibration using geometric invariants , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[20]  Narendra Ahuja,et al.  A Refractive Camera for Acquiring Stereo and Super-resolution Images , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[21]  Yasuyuki Matsushita,et al.  Self-calibrating depth from refraction , 2011, 2011 International Conference on Computer Vision.

[22]  Marie-José Aldon,et al.  Camera Self-Calibration in Underwater Environment , 2003, WSCG.

[23]  Shree K. Nayar,et al.  A Theory of Single-Viewpoint Catadioptric Image Formation , 1999, International Journal of Computer Vision.

[24]  Hongbin Zha,et al.  Self-Calibration of Catadioptric Camera with Two Planar Mirrors from Silhouettes , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Visesh Chari,et al.  Multi-View Geometry of the Refractive Plane , 2009, BMVC.

[26]  Visesh Chari,et al.  A theory of multi-layer flat refractive geometry , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[27]  H. Schröcker,et al.  Re(cid:176)ections on Refractions , 2000 .

[28]  Tsuhan Chen,et al.  Multi-view 3D reconstruction for scenes under the refractive plane with known vertical direction , 2011, 2011 International Conference on Computer Vision.

[29]  Tomás Svoboda,et al.  Epipolar Geometry for Central Catadioptric Cameras , 2002, International Journal of Computer Vision.

[30]  Paul A. Beardsley,et al.  Navigation using Affine Structure from Motion , 1994, ECCV.

[31]  Peter F. Sturm,et al.  General Imaging Geometry for Central Catadioptric Cameras , 2008, ECCV.

[32]  J. W. Bruce,et al.  On caustics of plane curves , 1981 .

[33]  Y.Y. Schechner,et al.  Flat refractive geometry , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[34]  Narendra Ahuja,et al.  Single camera stereo using planar parallel plate , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[35]  Kostas Daniilidis,et al.  Epipolar Geometry of Central Projection Systems Using Veronese Maps , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[36]  Yasuyuki Matsushita,et al.  Depth from Refraction Using a Transparent Medium with Unknown Pose and Refractive Index , 2013, International Journal of Computer Vision.

[37]  D. Burkhard,et al.  Flux density for ray propagation in geometrical optics , 1973 .

[38]  Shree K. Nayar,et al.  The Raxel Imaging Model and Ray-Based Calibration , 2005, International Journal of Computer Vision.