Auto-Setting of Optimal Exposure for Structured Light 3D Cameras

The quality of 3D point cloud captured by a structured light 3D camera, such as the density and accuracy of the points, depends upon the optimal settings of 3D camera exposure time. The fixed exposure time often set manually, may not be effective under the variation of ambient conditions, e.g., surface reflectance and illumination that incur saturation and/or under-exposure. This is especially the case where 3D cameras are to apply to industrial inspection. In this paper, a novel method for automatically determining the optimal exposure time of a 3D camera based on estimating the pixel intensity-shutter time slope is proposed. The experiments verify that a dense yet accurate 3D point cloud can be captured under varying ambient conditions: up to 40% improvement over the manual setting for the density of captured point cloud.

[1]  Lam Quang Bui,et al.  Boundary Inheritance Codec for high-accuracy structured light three-dimensional reconstruction with comparative performance evaluation. , 2013, Applied optics.

[2]  Song Zhang,et al.  Autoexposure for three-dimensional shape measurement using a digital-light-processing projector , 2011 .

[3]  Sukhan Lee,et al.  Signal Separation Coding for Robust Depth Imaging Based on Structured Light , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[4]  Dirk Kraft,et al.  A Structured Light Scanner for Hyper Flexible Industrial Automation , 2014, 2014 2nd International Conference on 3D Vision.

[5]  Sukhan Lee,et al.  Outlier removal based on boundary order and shade information in structured light 3D camera , 2015, 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM).

[6]  Lam Quang Bui,et al.  Accurate estimation of the boundaries of a structured light pattern. , 2011, Journal of the Optical Society of America. A, Optics, image science, and vision.

[7]  Tae-Yong Choi,et al.  MODMAN: SELF-RECONFIGURABLE MODULAR MANIPULATION SYSTEM FOR EXPANSION OF ROBOT APPLICABILITY , 2016 .

[8]  Joaquim Salvi,et al.  A state of the art in structured light patterns for surface profilometry , 2010, Pattern Recognit..

[9]  Laurence G. Hassebrook,et al.  Multicamera phase measuring profilometry for accurate depth measurement , 2007, SPIE Defense + Commercial Sensing.

[10]  Richard Szeliski,et al.  High-accuracy stereo depth maps using structured light , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[11]  Christopher Schwartz,et al.  A Multi-camera, Multi-projector Super-Resolution Framework for Structured Light , 2011, 2011 International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission.

[12]  Sukhan Lee,et al.  Boundary based shade detection , 2016, 2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI).

[13]  Zhongwei Li,et al.  Enhanced phase measurement profilometry for industrial 3D inspection automation , 2015 .

[14]  K. D. Mankoff,et al.  The Kinect: a low‐cost, high‐resolution, short‐range 3D camera , 2013 .