A MRF formulation for coded structured light

Multimedia projectors and cameras make possible the use of structured light to solve problems such as 3D reconstruction, disparity map computation and camera or projector calibration. Each projector displays patterns over a scene viewed by a camera, thereby allowing automatic computation of camera-projector pixel correspondences. This paper introduces a new algorithm to establish this correspondence in difficult cases of image acquisition. A probabilistic model formulated as a Markov random field uses the stripe images to find the most likely correspondences in the presence of noise. Our model is specially tailored to handle the unfavorable projector-camera pixel ratios that occur in multiple-projector single-camera setups. For the case where more than one camera is used, we propose a robust approach to establish correspondences between the cameras and compute an accurate disparity map. To conduct experiments, a ground truth was first reconstructed from a high quality acquisition. Various degradations were applied to the pattern images which were then solved using our method. The results were compared to the ground truth for error analysis and showed very good performances, even near depth discontinuities.

[1]  Nelson L. Chang,et al.  Efficient Dense Correspondences using Temporally Encoded Light Patterns , 2003 .

[2]  Sébastien Roy,et al.  Multi-projectors for arbitrary surfaces without explicit calibration nor reconstruction , 2003, Fourth International Conference on 3-D Digital Imaging and Modeling, 2003. 3DIM 2003. Proceedings..

[3]  Joaquim Salvi,et al.  Recent progress in structured light in order to solve the correspondence problem in stereovision , 1997, Proceedings of International Conference on Robotics and Automation.

[4]  Paolo Cignoni,et al.  A low cost 3D scanner based on structured light , 2001 .

[5]  Marc Levoy,et al.  Real-time 3D model acquisition , 2002, ACM Trans. Graph..

[6]  Aditi Majumder,et al.  PixelFlex 2 : A Comprehensive , Automatic , Casually-Aligned Multi-Projector Display , 2003 .

[7]  Ruigang Yang,et al.  PixelFlex: a reconfigurable multi-projector display system , 2001, Proceedings Visualization, 2001. VIS '01..

[8]  Joaquim Salvi,et al.  Implementation of a robust coded structured light technique for dynamic 3D measurements , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[9]  Richard Szeliski,et al.  A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms , 2001, International Journal of Computer Vision.

[10]  Stan Z. Li,et al.  Markov Random Field Modeling in Computer Vision , 1995, Computer Science Workbench.

[11]  Nahum Kiryati,et al.  Toward optimal structured light patterns , 1997, Proceedings. International Conference on Recent Advances in 3-D Digital Imaging and Modeling (Cat. No.97TB100134).

[12]  L. Van Gool,et al.  A graph cut based adaptive structured light approach for real-time range acquisition , 2004 .

[13]  W. Brent Seales,et al.  Multi-projector displays using camera-based registration , 1999, Proceedings Visualization '99 (Cat. No.99CB37067).

[14]  Joaquim Salvi,et al.  Pattern codification strategies in structured light systems , 2004, Pattern Recognit..

[15]  H. Maas.  Robust Automatic Surface Reconstruction with Structured Light , 1992 .

[16]  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..

[17]  Song Zhang,et al.  High-Resolution, Real-time 3D Shape Acquisition , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.