Unstructured light scanning to overcome interreflections

Reconstruction from structured light can be greatly affected by interreflections between surfaces in the scene. This paper introduces band-pass white noise patterns designed specifically to reduce interreflections, and still be robust to standard challenges in scanning systems such as scene depth discontinuities, defocus and low camera-projector pixel ratio. While this approach uses unstructured light patterns that increase the number of required projected images, it is up to our knowledge the first method that is able to recover scene disparities in the presence of both scene discontinuities and interreflections. Furthermore, the method does not require calibration (geometric nor photometric) or post-processing such as dynamic programming or phase unwrapping. We show results for a challenging scene and compare them to correspondences obtained with the well-known Gray code and Phase-shift methods.

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

[2]  Song Zhang,et al.  High-speed three-dimensional shape measurement system using a modified two-plus-one phase-shifting algorithm , 2007 .

[3]  André Oosterlinck,et al.  Range Image Acquisition with a Single Binary-Encoded Light Pattern , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Joaquim Salvi,et al.  A robust-coded pattern projection for dynamic 3D scene measurement , 1998, Pattern Recognit. Lett..

[5]  Richard Szeliski,et al.  The lumigraph , 1996, SIGGRAPH.

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

[7]  Li Zhang,et al.  Rapid shape acquisition using color structured light and multi-pass dynamic programming , 2002, Proceedings. First International Symposium on 3D Data Processing Visualization and Transmission.

[8]  Szymon Rusinkiewicz,et al.  Spacetime Stereo: A Unifying Framework for Depth from Triangulation , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Andrew W. Fitzgibbon,et al.  Learning epipolar geometry from image sequences , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[10]  Kim L. Boyer,et al.  Color-Encoded Structured Light for Rapid Active Ranging , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

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

[13]  Ramesh Raskar,et al.  Fast separation of direct and global components of a scene using high frequency illumination , 2006, ACM Trans. Graph..

[14]  Armin Gruen,et al.  Videometrics and Optical Methods for 3d Shape Measurement , 2000 .

[15]  Pieter Peers,et al.  Compressive light transport sensing , 2009, ACM Trans. Graph..

[16]  David W. Capson,et al.  Surface profile measurement using color fringe projection , 1991, Machine Vision and Applications.

[17]  Alexandr Andoni,et al.  Near-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions , 2006, 2006 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS'06).

[18]  N. Kiryati,et al.  Shape from Unstructured Light , 2007, 2007 3DTV Conference.

[19]  Luc Van Gool,et al.  One-shot active 3D shape acquisition , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[20]  Joseph Shamir,et al.  Range Imaging With Adaptive Color Structured Light , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  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..

[22]  Marc Levoy,et al.  Light field rendering , 1996, SIGGRAPH.

[23]  K. W. Cattermole The Fourier Transform and its Applications , 1965 .

[24]  Adnan A. Y. Mustafa Identifying and classifying image transforms , 2002, Object recognition supported by user interaction for service robots.

[25]  Piotr Indyk,et al.  Similarity Search in High Dimensions via Hashing , 1999, VLDB.