Using physically Based Rendering to Benchmark Structured Light Scanners
Structured light scanning is ubiquituous in 3D acquisition. It is capable of capturing high geometric detail at a low cost under a variety of challenging scene conditions. Recent methods have demonstrated robustness in the presence of artifacts due to global illumination, such as inter‐reflections a...
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| Published in | Computer graphics forum Vol. 33; no. 7; pp. 71 - 80 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Blackwell Publishing Ltd
01.10.2014
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0167-7055 1467-8659 |
| DOI | 10.1111/cgf.12475 |
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| Abstract | Structured light scanning is ubiquituous in 3D acquisition. It is capable of capturing high geometric detail at a low cost under a variety of challenging scene conditions. Recent methods have demonstrated robustness in the presence of artifacts due to global illumination, such as inter‐reflections and sub‐surface scattering, as well as imperfections caused by projector defocus. For comparing approaches, however, the quantitative evaluation of structured lighting schemes is hindered by the challenges in obtaining ground truth data, resulting in a poor understanding for these methods across a wide range of shapes, materials, and lighting configurations. In this paper, we present a benchmark to study the performance of structured lighting algorithms in the presence of errors caused due to the above properties of the scene. In order to do this, we construct a synthetic structured lighting scanner that uses advanced physically based rendering techniques to simulate the point cloud acquisition process. We show that, under conditions similar to that of a real scanner, our synthetic scanner replicates the same artifacts found in the output of a real scanner. Using this synthetic scanner, we perform a quantitative evaluation of four different structured lighting techniques – gray‐code patterns, micro‐phase shifting, ensemble codes, and unstructured light scanning. The evaluation, performed on a variety of scenes, demonstrate that no one method is capable of adequately handling all sources of error – each method is appropriate for addressing distinct sources of error. |
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| AbstractList | Structured light scanning is ubiquituous in 3D acquisition. It is capable of capturing high geometric detail at a low cost under a variety of challenging scene conditions. Recent methods have demonstrated robustness in the presence of artifacts due to global illumination, such as inter‐reflections and sub‐surface scattering, as well as imperfections caused by projector defocus. For comparing approaches, however, the quantitative evaluation of structured lighting schemes is hindered by the challenges in obtaining ground truth data, resulting in a poor understanding for these methods across a wide range of shapes, materials, and lighting configurations. In this paper, we present a benchmark to study the performance of structured lighting algorithms in the presence of errors caused due to the above properties of the scene. In order to do this, we construct a synthetic structured lighting scanner that uses advanced physically based rendering techniques to simulate the point cloud acquisition process. We show that, under conditions similar to that of a real scanner, our synthetic scanner replicates the same artifacts found in the output of a real scanner. Using this synthetic scanner, we perform a quantitative evaluation of four different structured lighting techniques – gray‐code patterns, micro‐phase shifting, ensemble codes, and unstructured light scanning. The evaluation, performed on a variety of scenes, demonstrate that no one method is capable of adequately handling all sources of error – each method is appropriate for addressing distinct sources of error. |
| Author | Silva, Claudio T. Doraiswamy, Harish Medeiros, Esdras Berger, Matthew |
| Author_xml | – sequence: 1 givenname: Esdras surname: Medeiros fullname: Medeiros, Esdras organization: Universidade Federal do Ceará – sequence: 2 givenname: Harish surname: Doraiswamy fullname: Doraiswamy, Harish organization: New York University – sequence: 3 givenname: Matthew surname: Berger fullname: Berger, Matthew organization: Air Force Research Laboratory – sequence: 4 givenname: Claudio T. surname: Silva fullname: Silva, Claudio T. organization: New York University |
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| Cites_doi | 10.1118/1.596777 10.1145/383259.383319 10.1007/s11263-012-0554-3 10.1364/AO.36.004463 10.1109/ICCV.2011.6126458 10.1117/12.230368 10.1145/2451236.2451246 10.1145/1141911.1141974 10.1364/AOP.3.000128 10.5772/26554 10.1117/1.1631921 10.1145/1141911.1141977 10.1145/882262.882343 10.1016/j.patcog.2003.10.002 10.1109/CVPR.2012.6247753 10.1016/j.patcog.2010.03.004 10.1109/TVCG.2008.97 |
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| Copyright | 2014 The Author(s) Computer Graphics Forum © 2014 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd. 2014 The Eurographics Association and John Wiley & Sons Ltd. |
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| References_xml | – reference: Gupta M., Agrawal A., Veeraraghavan A., Narasimhan S.: A practical approach to 3d scanning in the presence of interreflections, subsurface scattering and defocus. International Journal of Computer Vision 102, 1-3 (2013), 33-55. 1, 2, 4 – reference: Xu Y., Aliaga D.G.: An adaptive correspondence algorithm for modeling scenes with strong interreflections. IEEE Transactions on Visualization and Computer Graphics 15, 3 (2009), 465-480. 1, 3, 9 – reference: Seitz S., Curless B., Diebel J., Scharstein D., Szeliski R.: A comparison and evaluation of multi-view stereo reconstruction algorithms. In Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on (2006), vol. 1, pp. 519-528. 2 – reference: Szeliski R., Zabih R.: An experimental comparison of stereo algorithms. In Proceedings of the International Workshop on Vision Algorithms: Theory and Practice (2000), ICCV '99, Springer-Verlag, pp. 1-19. 2 – reference: Geng J.: Structured-light 3d surface imaging: a tutorial. Adv. Opt. Photon. 3, 2 (Jun 2011), 128-160. 1 – reference: Chen T., Seidel H.-P., Lensch H. P. A.: Modulated phase-shifting for 3d scanning. In 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008), 24-26 June 2008, Anchorage, Alaska, USA (2008), IEEE Computer Society. 1 – reference: Veach E., Guibas L.J.: Metropolis light transport. In Proceedings of the 24th annual conference on Computer graphics and interactive techniques (New York, NY, USA, 1997), SIGGRAPH '97, ACM Press/Addison-Wesley Publishing Co., pp. 65-76. 5 – reference: Salvi J., Pagãĺs J., Batlle J.: Pattern codification strategies in structured light systems. Pattern Recognition 37, 4 (2004), 827-849. 3 – reference: Nayar S.K., Krishnan G., Grossberg M.D., Raskar R.: Fast separation of direct and global components of a scene using high frequency illumination. ACM Trans. Graph. 25, 3 (July 2006), 935-944. 5 – reference: Berger M., Levine J.A., Nonato L.G., Taubin G., Silva C.T.: A benchmark for surface reconstruction. ACM Trans. Graph. 32, 2 (Apr. 2013), 20:1-20:17. 3 – reference: Pharr M., Hanrahan P.: Monte carlo evaluation of non-linear scattering equations for subsurface reflection. In Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques (New York, NY, USA, 2000), SIGGRAPH '00, ACM Press/Addison-Wesley Publishing Co., pp. 75-84. 5 – reference: Chauvenet W.: A Manual of Spherical and Practical Astronomy: Embracing the General Problems of Spherical Astronomy, the Special Applications to Nautical Astronomy, and the Theory and Use of Fixed and Portable Astronomical Instruments, with an Appendix on the Method of Least Squares. J. B. Lippincott & Company, 1863. 6 – reference: Scharstein D., Szeliski R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vision 47, 1-3 (Apr. 2002rpar;, 7-42. 2 – reference: Battle J., Mouaddib E., Salvi J.: Recent progress in coded structured light as a technique to solve the correspondence problem: A survey. Pattern Recognition 31, 7 (1998). 3 – reference: Matusik W., Pfister H., Brand M., McMillan L.: A data-driven reflectance model. ACM Transactions on Graphics 22, 3 (jul 2003), 759-769. 6 – reference: Lanman D., Taubin G.: Build your own 3d scanner: 3d photograhy for beginners. In SIGGRAPH '09: ACM SIGGRAPH 2009 courses (New York, NY, USA, 2009), ACM, pp. 1-87. 1, 2, 4 – reference: Salvi J., Fernandez S., Pribanic T., Llado X.: A state of the art in structured light patterns for surface profilometry. Pattern Recogn. 43, 8 (Aug. 2010), 2666-2680. 3 – reference: Blais F.: Review of 20 years of range sensor development. J. Electronic Imaging 13, 1 (2004), 231-243. 1 – reference: Cook R.L., Porter T., Carpenter L.: Distributed ray tracing. SIGGRAPH Comput. Graph. 18, 3 (Jan. 1984), 137-145. 5 – reference: Levoy M., Hanrahan P.: Light field rendering. In Proceedings of the 23rd Annual Conference on Computer Graphics and Interactive Techniques (New York, NY, USA, 1996), SIGGRAPH '96, ACM, pp. 31-42. 5 – reference: Farrell T.J., Patterson M.S., Wilson B.: A diffusion theory model of spatially resolved, steady-state diffuse reflectance for the noninvasive determination of tissue optical properties in vivo. Medical Physics 19, 4 (July 1992), 879-888. 5 – reference: Zhang L., Nayar S.: Projection defocus analysis for scene capture and image display. ACM Trans. Graph. 25, 3 (July 2006), 907-915. 4, 5, 6 – reference: Gupta M., Agrawal A., Veeraraghavan A., Narasimhan S.G.: Structured light 3d scanning in the presence of global illumination. In Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition (Washington, DC, USA, 2011), CVPR '11, IEEE Computer Society, pp. 713-720. 3 – reference: Sansoni G., Corini S., Lazzari S., Rodella R., Docchio F.: Three-dimensional imaging based on gray-code light projection: characterization of the measuring algorithm and development of a measuring system for industrial applications. Appl. Opt. 36, 19 (Jul 1997), 4463-4472. 1 – start-page: 227 year: 2012 end-page: 246 – start-page: 31 year: 1996 end-page: 42 – volume: 19 start-page: 879 year: 1992 end-page: 888 article-title: A diffusion theory model of spatially resolved, steady‐state diffuse reflectance for the noninvasive determination of tissue optical properties in vivo publication-title: Medical Physics – volume: 25 start-page: 935 year: 2006 end-page: 944 article-title: Fast separation of direct and global components of a scene using high frequency illumination publication-title: ACM Trans. Graph. – start-page: 713 year: 2011 end-page: 720 – volume: 18 start-page: 137 year: 1984 end-page: 145 publication-title: Distributed ray tracing – volume: 15 start-page: 465 year: 2009 end-page: 480 article-title: An adaptive correspondence algorithm for modeling scenes with strong interreflections publication-title: IEEE Transactions on Visualization and Computer Graphics – volume: 47 start-page: 1 end-page: 3 article-title: A taxonomy and evaluation of dense two‐frame stereo correspondence algorithms publication-title: Int. J. Comput. Vision – volume: 102 start-page: 1 year: 2013 end-page: 3 article-title: A practical approach to 3d scanning in the presence of interreflections, subsurface scattering and defocus publication-title: International Journal of Computer Vision – start-page: 65 end-page: 76 – volume: 22 start-page: 759 year: 2003 end-page: 769 article-title: A data‐driven reflectance model publication-title: ACM Transactions on Graphics – start-page: 813 year: 2012 end-page: 820 – start-page: 75 year: 2000 end-page: 84 – start-page: 511 year: 2001 end-page: 518 – volume: 37 start-page: 827 year: 2004 end-page: 849 article-title: Pattern codification strategies in structured light systems publication-title: Pattern Recognition – volume: 25 start-page: 907 year: 2006 end-page: 915 article-title: Projection defocus analysis for scene capture and image display publication-title: ACM Trans. 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| Snippet | Structured light scanning is ubiquituous in 3D acquisition. It is capable of capturing high geometric detail at a low cost under a variety of challenging scene... |
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| SubjectTerms | 3-D graphics Algorithms Analysis Categories and Subject Descriptors (according to ACM CCS) I.3.7 [Computer Graphics]: Raytracing I.4.1 [Digitization and Image Capture]: Reflectance-Scanning Lighting Scanners Scanning Studies |
| Title | Using physically Based Rendering to Benchmark Structured Light Scanners |
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