3D depth information extraction with omni-directional camera

This paper presents a novel 3D depth information extraction method without calibration. Firstly, this paper develops an omni-directional 3D camera system, which consists of a CCD camera, hyperbolic mirror, infrared laser diodes and diffractive of element (DOE). Secondly, a depth measurement model is proposed to obtain the 3D depth information. Finally, in order to calculate the speckle shift accurately between the reference image and the object image, a dot matrix pattern and sequence coding algorithm are designed to find the corresponding speckles in the two images. Experimental results show that the reconstructed depth data have a good correlation with the actual distance. The accuracy of the data is also found to be influenced by the distance between the object and the camera. A novel omni-directional 3D camera framework is proposed.The omni-directional camera and infrared dot matrix structured light can be merged.The model can reveal the relation between the object depth and its pixel offset.We can obtain the pixel offset with the sequence coding algorithm.

[1]  Haiyong Zheng,et al.  Depth measurement of underwater target based on laser frequency-difference scanning , 2012, 2012 Oceans.

[2]  Yasushi Yagi,et al.  Dynamic scene shape reconstruction using a single structured light pattern , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Ken Chen,et al.  An approach for structured light system calibration , 2013, 2013 IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems.

[4]  Charles Beumier 3D Face Recognition , 2006 .

[5]  Kaspar Althoefer,et al.  Novel indentation depth measuring system for stiffness characterization in soft tissue palpation , 2012, 2012 IEEE International Conference on Robotics and Automation.

[6]  Chia-Hsiang Wu,et al.  Three-Dimensional Modeling From Endoscopic Video Using Geometric Constraints Via Feature Positioning , 2007, IEEE Transactions on Biomedical Engineering.

[7]  Yongkang Guo,et al.  Large depth-of-view portable three-dimensional laser scanner and its segmental calibration for robot vision , 2007 .

[8]  Fang-Hsuan Cheng,et al.  3D Object Scanning System by Coded Structured Light , 2010, 2010 Third International Symposium on Electronic Commerce and Security.

[9]  Guangjun Zhang,et al.  A novel 1D target-based calibration method with unknown orientation for structured light vision sensor , 2010 .

[10]  Shree K. Nayar,et al.  A theory of catadioptric image formation , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[11]  Anneli Folkesson Electronic commerce and security , 2007 .

[12]  Noboru Noguchi,et al.  Human detection for a robot tractor using omni-directional stereo vision , 2012 .

[13]  Amnon Shashua,et al.  Trajectory Triangulation: 3D Reconstruction of Moving Points from a Monocular Image Sequence , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Luc Van Gool,et al.  Real-time range acquisition by adaptive structured light , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Tianran Yuan,et al.  Calibration Algorithm for Structured Light 3D Vision Measuring System , 2008, 2008 Congress on Image and Signal Processing.