Uncertainty in 3D reconstruction of underwater objects due to refraction

Refraction of light underwater is a major source of error for 3D reconstruction. Use of a physically correct camera model taking into account properties of the optical system is essential for understanding the reasons why errors occur and obtaining quantitative estimates of errors. It was proven that use of the single viewpoint model of the camera leads to significant distortions in reconstruction. This paper proposes a novel technique to determine the physical parameters that influence refractive effects. Relative importance of various parameters was investigated in simulations and experiments with real targets underwater. The results of 3D reconstructions are compared with known ground truth in case of numerical modeling and with models acquired by Kinect2 in case of experiments underwater.

[1]  Yee-Hong Yang,et al.  Two-View Camera Housing Parameters Calibration for Multi-layer Flat Refractive Interface , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Pietro Cerveri,et al.  Comparison of different camera calibration approaches for underwater applications. , 2012, Journal of biomechanics.

[3]  Ingo Wald,et al.  Embree: a kernel framework for efficient CPU ray tracing , 2014, ACM Trans. Graph..

[4]  Uwe von Lukas,et al.  Calibration of Shared Flat Refractive Stereo Systems , 2016, ICIAR.

[5]  Radu Bogdan Rusu,et al.  3D is here: Point Cloud Library (PCL) , 2011, 2011 IEEE International Conference on Robotics and Automation.

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

[7]  Anne Jordt,et al.  Calibration of Housing Parameters for Underwater Stereo-Camera Rigs , 2011, BMVC.

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

[9]  ProblemsPer Christian HansenDepartment The L-curve and its use in the numerical treatment of inverse problems , 2000 .

[10]  Anne Jordt,et al.  Perspective and Non-perspective Camera Models in Underwater Imaging - Overview and Error Analysis , 2011, Theoretical Foundations of Computer Vision.

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

[12]  Timothy Hy Yau Underwater Camera Calibration and 3D Reconstruction , 2014 .

[13]  O. Pizarro,et al.  Visually Augmented Navigation for Autonomous Underwater Vehicles , 2008, IEEE Journal of Oceanic Engineering.