Eliminating the Effect of Reflectance Properties on Reconstruction in Stripe Structured Light System

The acquisition of the geometry of general scenes is related to the interplay of surface geometry, material properties and illumination characteristics. Surface texture and non-Lambertian reflectance properties degrade the reconstruction results by structured light technique. Existing structured light techniques focus on different coding strategy and light sources to improve reconstruction accuracy. The hybrid system consisting of a structured light technique and photometric stereo combines the depth value with normal information to refine the reconstruction results. In this paper, we propose a novel hybrid system consisting of stripe-based structured light and photometric stereo. The effect of surface texture and non-Lambertian reflection on stripe detection is first concluded. Contrary to existing fusion strategy, we propose an improved method for stripe detection to reduce the above factor’s effects on accuracy. The reconstruction problem for general scene comes down to using reflectance properties to improve the accuracy of stripe detection. Several objects, including checkerboard, metal-flat plane and free-form objects with complex reflectance properties, were reconstructed to validate our proposed method, which illustrates the effectiveness on improving the reconstruction accuracy of complex objects. The three-step phase-shifting algorithm was implemented and the reconstruction results were given and also compared with ours. In addition, our proposed framework provides a new feasible scheme for solving the ongoing problem of the reconstruction of complex objects with variant reflectance. The problem can be solved by subtracting the non-Lambertian components from the original grey values of stripe to improve the accuracy of stripe detection. In the future, based on stripe structured light technique, more general reflection models can be used to model different types of reflection properties of complex objects.

[1]  Zhan Gao,et al.  Structured Light Three-Dimensional Measurement Based on Machine Learning , 2019, Sensors.

[2]  Venu Madhav Govindu,et al.  High Quality Photometric Reconstruction Using a Depth Camera , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  James J. Clark.  Photometric Stereo with Nearby Planar Distributed Illuminants , 2006, The 3rd Canadian Conference on Computer and Robot Vision (CRV'06).

[4]  Michael Breuß,et al.  Combining Shape from Shading and Stereo: A Joint Variational Method for Estimating Depth, Illumination and Albedo , 2018, International Journal of Computer Vision.

[5]  Szymon Rusinkiewicz,et al.  Efficiently combining positions and normals for precise 3D geometry , 2005, ACM Trans. Graph..

[6]  Jeffrey L. Posdamer,et al.  Surface measurement by space-encoded projected beam systems , 1982, Comput. Graph. Image Process..

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

[8]  Ming Li,et al.  Improved Visual Inspection through 3D Image Reconstruction of Defects Based on the Photometric Stereo Technique , 2019, Sensors.

[9]  Daniel Cremers,et al.  LED-Based Photometric Stereo: Modeling, Calibration and Numerical Solution , 2017, Journal of Mathematical Imaging and Vision.

[10]  M. Trobina.  Error Model of a Coded-Light Range Sensor , 2007 .

[11]  Ronald Chung,et al.  Use of LCD Panel for Calibrating Structured-Light-Based Range Sensing System , 2008, IEEE Transactions on Instrumentation and Measurement.

[12]  James F. Blinn,et al.  Models of light reflection for computer synthesized pictures , 1977, SIGGRAPH.

[13]  Fu Li,et al.  Single-Shot Colored Speckle Pattern for High Accuracy Depth Sensing , 2019, IEEE Sensors Journal.

[14]  Jean-Denis Durou,et al.  Variational Methods for Normal Integration , 2017, Journal of Mathematical Imaging and Vision.

[15]  Hongzhi Jiang,et al.  High dynamic range fringe acquisition: A novel 3-D scanning technique for high-reflective surfaces , 2012 .

[16]  Venu Madhav Govindu,et al.  Photometric refinement of depth maps for multi-albedo objects , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Mara Pistellato,et al.  Adaptive Albedo Compensation for Accurate Phase-Shift Coding , 2018, 2018 24th International Conference on Pattern Recognition (ICPR).

[18]  S. Inokuchi,et al.  Range-imaging system for 3-D object recognition , 1984 .

[19]  Ronald Chung,et al.  An Accurate and Robust Strip-Edge-Based Structured Light Means for Shiny Surface Micromeasurement in 3-D , 2013, IEEE Transactions on Industrial Electronics.

[20]  Hanjun Jiang,et al.  A 3-D Surface Reconstruction with Shadow Processing for Optical Tactile Sensors , 2018, Sensors.

[21]  Wenbin Li,et al.  A Range-Independent Disparity-Based Calibration Model for Structured Light Pattern-Based RGBD Sensor , 2020, Sensors.

[22]  Sandro Barone,et al.  A Coded Structured Light System Based on Primary Color Stripe Projection and Monochrome Imaging , 2013, Sensors.

[23]  Svorad Stolc,et al.  A Review of Depth and Normal Fusion Algorithms , 2018, Sensors.

[24]  Ashok Veeraraghavan,et al.  A Practical Approach to 3D Scanning in the Presence of Interreflections, Subsurface Scattering and Defocus , 2013, International Journal of Computer Vision.

[25]  Reinhard Klein,et al.  Advances in geometry and reflectance acquisition (course notes) , 2015, SIGGRAPH Asia Courses.

[26]  Robert Sitnik,et al.  Integrated three-dimensional shape and reflection properties measurement system. , 2011, Applied optics.

[27]  Xiangjun Wang,et al.  Flexible Three-Dimensional Reconstruction via Structured-Light-Based Visual Positioning and Global Optimization , 2019, Sensors.

[28]  Anand Asundi,et al.  Fast three-dimensional measurements for dynamic scenes with shiny surfaces , 2017 .

[29]  Huijie Zhao,et al.  Rapid in-situ 3D measurement of shiny object based on fast and high dynamic range digital fringe projector , 2014 .

[30]  Michal Haindl,et al.  Visual Texture: Accurate Material Appearance Measurement, Representation and Modeling , 2013 .

[31]  Yuping Ye,et al.  Accurate infrared structured light sensing system for dynamic 3D acquisition. , 2020, Applied optics.

[32]  Le-Nan Wu,et al.  An adaptive threshold for the Canny Operator of edge detection , 2010, 2010 International Conference on Image Analysis and Signal Processing.

[33]  Shiqian Wu,et al.  An Accurate Calibration Means for the Phase Measuring Deflectometry System , 2019, Sensors.

[34]  Guangming Shi,et al.  Single-Shot Dense Depth Sensing with Color Sequence Coded Fringe Pattern , 2017, Sensors.

[35]  Jens Guehring,et al.  Dense 3D surface acquisition by structured light using off-the-shelf components , 2000, IS&T/SPIE Electronic Imaging.

[36]  Sukhan Lee,et al.  Antipodal gray codes for structured light , 2008, 2008 IEEE International Conference on Robotics and Automation.

[37]  Jostein Thorstensen,et al.  Adaptive Structured Light with Scatter Correction for High-Precision Underwater 3D Measurements , 2019, Sensors.

[38]  Hui Lin,et al.  Adaptive digital fringe projection technique for high dynamic range three-dimensional shape measurement. , 2016, Optics express.

[39]  Zhao Song,et al.  Photometric stereo with quasi-point light source , 2018, Optics and Lasers in Engineering.

[40]  Carl Olsson,et al.  Combining Depth Fusion and Photometric Stereo for Fine-Detailed 3D Models , 2019, SCIA.

[41]  Hui Yu,et al.  Deviation correction method for close-range photometric stereo with nonuniform illumination , 2017 .