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Reflectance and Shape Estimation with a Light Field Camera Under Natural Illumination

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Abstract

Reflectance and shape are two important components in visually perceiving the real world. Inferring the reflectance and shape of an object through cameras is a fundamental research topic in the field of computer vision. While three-dimensional shape recovery is pervasive with varieties of approaches and practical applications, reflectance recovery has only emerged recently. Reflectance recovery is a challenging task that is usually conducted in controlled environments, such as a laboratory environment with a special apparatus. However, it is desirable that the reflectance be recovered in the field with a handy camera so that reflectance can be jointly recovered with the shape. To that end, we present a solution that simultaneously recovers the reflectance and shape (i.e., dense depth and normal maps) of an object under natural illumination with commercially available handy cameras. We employ a light field camera to capture one light field image of the object, and a 360-degree camera to capture the illumination. The proposed method provides positive results in both simulation and real-world experiments.

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References

  • Abrams, A. H., & Christopherand Pless, R. (2012). Heliometric stereo: Shape from sun position. In A. Fitzgibbon, S. Lazebnik, P. Perona, Y. Sato, & C. Schmid (Eds.), ECCV 2012 (pp. 357–370). Berlin: Springer.

    Google Scholar 

  • Ackermann, J., Langguth, F., Fuhrmann, S., & Goesele, M. (2012). Photometric stereo for outdoor webcams. In 2012 IEEE conference on computer vision and pattern recognition, (pp. 262–269). https://doi.org/10.1109/CVPR.2012.6247684.

  • Alldrin, N., Zickler, T., & Kriegman, D. (2008). Photometric stereo with non-parametric and spatially-varying reflectance. In Proceedings of IEEE computer vision and pattern recognition (CVPR), p. 8. https://doi.org/10.1109/CVPR.2008.4587656.

  • Barron, J., & Malik, J. (2013). Shape, illumination, and reflectance from shading. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1(c), 1–19. https://doi.org/10.1109/TPAMI.2014.2377712.

    Article  Google Scholar 

  • Ben-ezra, M., Wang, J., & Wilburn, B. (2008). An LED-only BRDF measurement device. In Proceedings of the IEEE conference on computer vision and pattern recognition, (pp. 1–8).

  • Delaunoy, A., & Pollefeys, M. (2014). Photometric bundle adjustment for dense multi-view 3D modeling. In IEEE computer society conference on computer vision and pattern recognition, (pp. 1486–1492).

  • der Jeught, S. V., & Dirckx, J. J. J. (2016). Real-time structured light profilometry: A review. Optics and Lasers in Engineering, 87, 18–31. https://doi.org/10.1016/j.optlaseng.2016.01.011.

  • Dror, R. O., Adelson, E. H., Willsky, A. S. (2001). Estimating surface reflectance properties from images under unknown illumination. In Proceedings of SPIE 4299, human vision and electronic imaging VI, vol. 4299, (pp. 231–243). https://doi.org/10.1117/12.429494.

  • Godard, C., Hedman, P., Li, W., & Brostow, G.J. (2015). Multi-view reconstruction of highly specular surfaces in uncontrolled environments. In Proceedings—2015 international conference on 3D Vision, 3DV 2015 (pp. 19–27). https://doi.org/10.1109/3DV.2015.10.

  • Goldman, D.B., Curless, B., Hertzmann, A., & Seitz, S.M. (2005). Shape and spatially-varying brdfs from photometric stereo. In Tenth IEEE international conference on computer vision (ICCV’05) vol 1, (pp. 341–348). https://doi.org/10.1109/ICCV.2005.219.

  • Hertzmann, A., & Seitz, S. M. (2005). Example-based photometric stereo: Shape reconstruction with general, varying BRDFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(8), 1254–1264. https://doi.org/10.1109/TPAMI.2005.158.

    Article  Google Scholar 

  • Higo, T., Matsushita, Y., Ikeuchi, K.: Consensus photometric stereo. In 2010 IEEE computer society conference on computer vision and pattern recognition, pp. 1157–1164. IEEE (2010). https://doi.org/10.1109/CVPR.2010.5540084.

  • Hold-Geoffroy, Y., Zhang, J., Gotardo, P.F.U., & Lalonde, J.F. (2015). What is a good day for outdoor photometric stereo? In 2015 IEEE international conference on computational photography (ICCP), (pp. 1–9). https://doi.org/10.1109/ICCPHOT.2015.7168379.

  • Huang, R., & Smith, W. A. P. (2011). Shape-from-shading under complex natural illumination. In Proceedings—international conference on image processing, ICIP (pp. 13–16). https://doi.org/10.1109/ICIP.2011.6115701.

  • Hui, Z., & Sankaranarayanan, A. C. (2017). Shape and spatially-varying reflectance estimation from virtual exemplars. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(10), 2060–2073. https://doi.org/10.1109/TPAMI.2016.2623613.

    Article  Google Scholar 

  • Jeon, H. G., Park, J., Choe, G., Park, J., Bok, Y., Tai, Y. W., et al. (2015). Accurate depth map estimation from a lenslet light field camera. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 07–12–June, 1547–1555. https://doi.org/10.1109/CVPR.2015.7298762.

    Article  Google Scholar 

  • Johnson, M. K., & Adelson, E. H. (2011). Shape estimation in natural illumination. In Proceedings of the IEEE computer society conference on computer vision and pattern recognition (pp. 2553–2560). https://doi.org/10.1109/CVPR.2011.5995510.

  • Jung, J., Lee, J. Y., & Kweon, I. S. (2015). One-day outdoor photometric stereo via skylight estimation. In 2015 IEEE conference on computer vision and pattern recognition (CVPR), (pp. 4521–4529). https://doi.org/10.1109/CVPR.2015.7299082.

  • Kolmogorov, V., Monasse, P., & Tan, P. (2014). Kolmogorov and Zabih’s graph cuts stereo matching algorithm. IPOL Journal, 4, 220–251. https://doi.org/10.5201/ipol.2014.97.

    Article  Google Scholar 

  • Lombardi, S., & Nishino, K. (2016). Reflectance and illumination recovery in the wild. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(1), 129–141. https://doi.org/10.1109/TPAMI.2015.2430318.

    Article  Google Scholar 

  • Marschner, S. R., Westin, S. H., Lafortune, E. P., & Torrance, K. E. (2000). Image-based bidirectional reflectance distribution function measurement. Applied Optics, 39(16), 2592–2600. https://doi.org/10.1364/AO.39.002592.

    Article  Google Scholar 

  • Matusik, W., Pfister, H., Brand, M., & McMillan, L. (2003). A data-driven reflectance model. ACM Transactions on Graphics, 22(3), 759–769. https://doi.org/10.1145/882262.882343.

    Article  Google Scholar 

  • Mukaigawa, Y., Tagawa, S., Kim, J., Raskar, R., Matsushita, Y., & Yagi, Y. (2011). Hemispherical confocal imaging using turtleback reflector. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6492 LNCS (PART 1), pp. 336–349.

  • Ngo, T., Nagahara, H., & Taniguchi, R. (2015). Shape and light directions from shading and polarization. In Proceedings of the IEEE conference on computer vision and pattern recognition, (pp. 2310–2318).

  • Ngo, T., Nagahara, H., Nishino, K., Taniguchi, R., & Yagi, Y. (2017). Reflectance and shape estimation with a light field camera under natural illumination. In 28th British machine vision conference.

  • Nishino, K., Zhang, Z., & Ikeuchi, K. (2001). Determining reflectance parameters and illumination distribution from a sparse set of images for view-dependent image synthesis. In Proceedings Eighth IEEE international conference on computer vision. ICCV 2001, vol. 1, (pp. 599–606). https://doi.org/10.1109/ICCV.2001.937573.

  • Nishino, K., & Lombardi, S. (2011). Directional statistics-based reflectance model for isotropic bidirectional reflectance distribution functions. Journal of the Optical Society of America A, 28(1), 8–18.

    Article  Google Scholar 

  • Oren, M., & Nayar, S. K. (1994). Generalization of Lambert’s reflectance model. In Proceedings of the 21st annual conference on computer graphics and interactive techniques—SIGGRAPH ’94 (pp. 239–246). https://doi.org/10.1145/192161.192213.

  • Oxholm, G., & Nishino, K. (2014). Multiview shape and reflectance from natural illumination. In IEEE conference on computer vision and pattern recognition (pp. 2163–2170).

  • Oxholm, G., & Nishino, K. (2012). Shape and reflectance from natural illumination. Computer Vision - ECCV, 2012(7572), 528–541. https://doi.org/10.1109/CVPR.2014.277.

    Article  Google Scholar 

  • Oxholm, G., & Nishino, K. (2016). Shape and reflectance estimation in the wild. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(2), 376–389.

    Article  Google Scholar 

  • Patow, G., & Pueyo, X. (2003). A survey of inverse rendering problems. Computer Graphics Forum, 22(4), 663–687. https://doi.org/10.1111/j.1467-8659.2003.00716.x.

    Article  MATH  Google Scholar 

  • Pharr, M., & Humphreys, G. (2010). Physically based rendering, second edition: From theory to implementation (2nd ed.). San Francisco, CA: Morgan Kaufmann Publishers Inc.

    Google Scholar 

  • Phong, B. T. (1975). Illumination for computer generated pictures. Communications of the ACM, 18(6), 311–317. https://doi.org/10.1145/360825.360839.

    Article  Google Scholar 

  • Ramamoorthi, R., & Hanrahan, P. (2001). A signal-processing framework for inverse rendering. In Proceedings of the 28th annual conference on computer graphics and interactive techniques, SIGGRAPH ’01, (pp. 117–128). ACM, New York https://doi.org/10.1145/383259.383271.

  • Ricoh Company. (2016). Theta S camera. https://theta360.com/en/about/theta/s.html.

  • Rusinkiewicz, S. M. (1998). A new change of variables for efficient BRDF representation. In Rendering techniques, proceedings of eurographics rendering workshop (pp. 11–22).

  • Seitz, S., & Dyer, C. (1997). Photorealistic scene reconstruction by voxel coloring. In Proceedings of computer vision and pattern recognition (pp. 1067–1073).

  • Smith, W. A., Ramamoorthi, R., & Tozza, S. (2016). Linear depth estimation from an uncalibrated, monocular polarisation image. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 9912 LNCS, pp. 109–125.

  • Szeliski, R. (2010). Computer vision: Algorithms and applications. Berlin: Springer.

    MATH  Google Scholar 

  • Tao, M. W., & Srinivasan, P. P. (2015). Depth from shading, defocus, and correspondence using light-field angular coherence. In CVPR (pp. 1940–1948).

  • Tao, M.W., Hadap, S., Malik, J., & Ramamoorthi, R. (2013). Depth from combining defocus and correspondence using light-field cameras. In 2013 IEEE international conference on computer vision 2, pp. 673–680. https://doi.org/10.1109/ICCV.2013.89.

  • Tao, M. W., Wang, T. C., Malik, J., & Ramamoorthi, R. (2014). Depth estimation for glossy surfaces with light-field cameras. In ECCV workshop light fields computer vision, (pp. 1–14).

  • Torrance, K. E., & Sparrow, E. M. (1967). Theory for off-specular reflection from roughened surfaces\(\ast \). J. Opt. Soc. Am., 57(9), 1105–1114. https://doi.org/10.1364/JOSA.57.001105.

    Google Scholar 

  • USC Institute for Creative Technologies. (2008). High-Resolution Light Probe Image Gallery. http://gl.ict.usc.edu/Data/HighResProbes/.

  • Vogiatzis, G., & Cipolla, R. (2008). Multiview photometric stereo. PAMI, 30(3), 548–554.

    Article  Google Scholar 

  • Wang, T. C., Chandraker, M., Efros, A. A., & Ramamoorthi, R. (2016). SVBRDF-Invariant shape and reflectance estimation from light-field cameras differential stereo. In CVPR (pp. 5451–5459). https://doi.org/10.1109/CVPR.2016.588.

  • Wanner, S., & Goldluecke, B. (2012). Globally consistent depth labeling of 4D light fields. In Proceedings of the IEEE computer society conference on computer vision and pattern recognition, (pp. 41–48) https://doi.org/10.1109/CVPR.2012.6247656.

  • Wolff, L. B. (1996). Generalizing Lambert’s law for smooth surfaces. In ECCV, (pp. 40–53).

  • Woodham, R. J. (1980). Photometric method for determining surface orientation from multiple images. Optical Engineering. https://doi.org/10.1117/12.7972479.

  • Xia, R., Dong, Y., Peers, P., & Tong, X. (2016). Recovering shape and spatially-varying surface reflectance under unknown illumination. ACM Transactions on Graphics, 35(6), 1–12. https://doi.org/10.1145/2980179.2980248.

    Article  Google Scholar 

  • Yu, L. F., Yeung, S. K., Tai, Y. W., Terzopoulos, D., & Chan, T. F. (2013). Outdoor photometric stereo. In IEEE international conference on computational photography (ICCP), (pp. 1–8). https://doi.org/10.1109/ICCPhot.2013.6528306.

  • Zhou, Z., Wu, Z., & Tan, P. (2013). Multi-view Photometric stereo with spatially varying isotropic materials. In 2013 IEEE conference on computer vision and pattern recognition, (pp. 1482–1489). https://doi.org/10.1109/CVPR.2013.195.

  • Zhou, C., & Nayar, S. K. (2011). Computational cameras: Convergence of optics and processing. IEEE Transactions on Image Processing, 20(12), 3322–3340. https://doi.org/10.1109/TIP.2011.2171700.

    Article  Google Scholar 

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Acknowledgements

We thank anonymous reviewers for their suggestions on how to improve the quality of the manuscript and for an interesting discussion about a future work. We thank Glenn Pennycook, MSc, from Edanz Group (www.edanzediting.com/ac) for editing a draft of this manuscript. Funding was provided by Japan Society for the Promotion of Science (Grant No. JP16H01675).

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Authors and Affiliations

  1. Osaka University, Osaka, Japan

    Thanh-Trung Ngo, Hajime Nagahara & Yasushi Yagi

  2. Kyoto University, Kyoto, Japan

    Ko Nishino

  3. Kyshu University, Fukuoka, Japan

    Rin-ichiro Taniguchi

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  1. Thanh-Trung Ngo
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  2. Hajime Nagahara
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Correspondence to Thanh-Trung Ngo.

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Ngo, TT., Nagahara, H., Nishino, K. et al. Reflectance and Shape Estimation with a Light Field Camera Under Natural Illumination. Int J Comput Vis 127, 1707–1722 (2019). https://doi.org/10.1007/s11263-019-01149-5

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