An Evaluation of Computational Imaging Techniques for Heterogeneous Inverse Scattering

详细信息    查看全文

• 关键词：Inverse scattering ; Computational imaging
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9907
• 期：1
• 页码：685-701
• 全文大小：2,223 KB
• 参考文献：1.Project page. http://​vision.​seas.​harvard.​edu/​inverse_​transient/​
  2.Antyufeev, V.: Monte Carlo Method for Solving Inverse Problems of Radiation Transfer. Inverse and Ill-Posed Problems Series, vol. 20. V.S.P. International Science, Utrecht (2000)MATH
  3.Arridge, S.R.: Optical tomography in medical imaging. Inverse Prob. 15, R41–R93 (1999)MathSciNet CrossRef MATH
  4.Bal, G.: Inverse transport theory and applications. Inverse Prob. 25(5) (2009)
  5.Boas, D.A., Brooks, D.H., Miller, E.L., DiMarzio, C.A., Kilmer, M., Gaudette, R.J., Zhang, Q.: Imaging the body with diffuse optical tomography. IEEE Signal Process. Mag. 18, 57–75 (2001)CrossRef
  6.Boyd, R.W.: Radiometry and the Detection of Optical Radiation. Wiley, New York (1983)
  7.Case, K.M., Zweifel, P.F.: Linear Transport Theory. Addison-Wesley Pub. Co., Boston (1967)MATH
  8.Debevec, P., Hawkins, T., Tchou, C., Duiker, H., Sarokin, W., Sagar, M.: Acquiring the reflectance field of a human face. In: Proceedings of SIGGRAPH 2000, Annual Conference Series (2000)
  9.Donner, C., Jensen, H.: Light diffusion in multi-layered translucent materials. ACM Trans. Graph. 24(3), 1032–1039 (2005)CrossRef
  10.Donner, C., Weyrich, T., d’Eon, E., Ramamoorthi, R., Rusinkiewicz, S.: A layered, heterogeneous reflectance model for acquiring and rendering human skin. ACM Trans. Graph. 27(5) (2008)
  11.Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12, 2121–2159 (2011)MathSciNet MATH
  12.Fuchs, C., Chen, T., Goesele, M., Theisel, H., Seidel, H.: Density estimation for dynamic volumes. Comput. Graph. 31(2), 205–211 (2007)CrossRef
  13.Gkioulekas, I., Levin, A., Durand, F., Zickler, T.: Micron-scale light transport decomposition using interferometry. ACM Trans. Graph. (2015)
  14.Gkioulekas, I., Zhao, S., Bala, K., Zickler, T., Levin, A.: Inverse volume rendering with material dictionaries. ACM Trans. Graph. (2013)
  15.Goesele, M., Lensch, H., Lang, J., Fuchs, C., Seidel, H.: Disco: acquisition of translucent objects. ACM Trans. Graph. 23(3), 835–844 (2004)CrossRef
  16.Goodman, J.W.: Introduction to Fourier Optics. McGraw-Hill Book Company, New York (1968)
  17.Gu, J., Nayar, S.K., Grinspun, E., Belhumeur, P.N., Ramamoorthi, R.: Compressive structured light for recovering inhomogeneous participating media. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 845–858. Springer, Heidelberg (2008)CrossRef
  18.Gupta, M., Agrawal, A., Veeraraghavan, A., Narasimhan, S.G.: Structured light 3D scanning in the presence of global illumination. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 713–720, June 2011
  19.Hasinoff, S., Durand, F., Freeman, W.: Noise-optimal capture for high dynamic range photography. In: IEEE CVPR (2010)
  20.Hawkins, T., Einarsson, P., Debevec, P.: Acquisition of time-varying participating media. ACM Trans. Graph. 24(3), 812–815 (2005)CrossRef
  21.Heide, F., Hullin, M.B., Gregson, J., Heidrich, W.: Low-budget transient imaging using photonic mixer devices. ACM Trans. Graph. 32(4), 45:1–45:10 (2013)MATH
  22.Heide, F., Xiao, L., Kolb, A., Hullin, M.B., Heidrich, W.: Imaging in scattering media using correlation image sensors and sparse convolutional coding. Opt. Express 22(21), 26338–26350 (2014)CrossRef
  23.Henyey, L., Greenstein, J.: Diffuse radiation in the galaxy. Astrophys. J. 93, 70–83 (1941)CrossRef MATH
  24.Huang, D., Swanson, E., Lin, C., Schuman, J., Stinson, W., Chang, W., Hee, M., Flotte, T., Gregory, K., Puliafito, C., Fujimoto, G.: Optical coherence tomography. Science 254(5035), 1178–1181 (1991)CrossRef
  25.Ishimaru, A.: Wave Propagation and Scattering in Random Media. Wiley-IEEE, New York (1978)MATH
  26.Jakob, W.: Mitsuba renderer (2010). http://​www.​mitsuba-renderer.​org
  27.Jarabo, A., Marco, J., Muñoz, A., Buisan, R., Jarosz, W., Gutierrez, D.: A framework for transient rendering. ACM Trans. Graph. 33(6), 177:1–177:10 (2014)CrossRef
  28.Jensen, H., Marschner, S., Levoy, M., Hanrahan, P.: A practical model for subsurface light transport. In: Proceedings of SIGGRAPH 2001, Annual Conference Series (2001)
  29.Kadambi, A., Whyte, R., Bhandari, A., Streeter, L., Barsi, C., Dorrington, A., Raskar, R.: Coded time of flight cameras: sparse deconvolution to address multipath interference and recover time profiles. ACM Trans. Graph. 32(6), 167:1–167:10 (2013)CrossRef
  30.Khungurn, P., Schroeder, D., Zhao, S., Bala, K., Marschner, S.: Matching real fabrics with micro-appearance models. ACM Trans. Graph. 35(1), 1:1–1:26 (2015)CrossRef
  31.Kingma, D., Ba, J.: Adam: A method for stochastic optimization. In: ICLR (2015)
  32.Levis, A., Schechner, Y., Aides, A., Davis, A.: Airborne three-dimensional cloud tomography. In: IEEE International Conference on Computer Vision (2015)
  33.Mukaigawa, Y., Yagi, Y., Raskar, R.: Analysis of light transport in scattering media. In: IEEE CVPR (2010)
  34.Narasimhan, S., Gupta, M., Donner, C., Ramamoorthi, R., Nayar, S., Jensen, H.: Acquiring scattering properties of participating media by dilution. ACM Trans. Graph. 25(3), 1003–1012 (2006)CrossRef
  35.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), 935–944 (2006)CrossRef
  36.O’Toole, M., Mather, J., Kutulakos, K.: 3D Shape and indirect appearance by structured light transport. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3246–3253, June 2014
  37.O’Toole, M., Heide, F., Xiao, L., Hullin, M.B., Heidrich, W., Kutulakos, K.N.: Temporal frequency probing for 5D transient analysis of global light transport. ACM Trans. Graph. 33(4), 87:1–87:11 (2014)
  38.O’Toole, M., Raskar, R., Kutulakos, K.N.: Primal-dual coding to probe light transport. ACM Trans. Graph. 31(4), 39:1–39:11 (2012)
  39.Papas, M., Regg, C., Jarosz, W., Bickel, B., Jackson, P., Matusik, W., Marschner, S., Gross, M.: Fabricating translucent materials using continuous pigment mixtures. ACM Trans. Graph. 32(4), 146:1–146:12 (2013)CrossRef MATH
  40.Pauly, M., Kollig, T., Keller, A.: Metropolis light transport for participating media. In: Péroche, B., Rushmeier, H. (eds.) Rendering Techniques 2000, pp. 11–22. Springer, Vienna (2000)CrossRef
  41.Peers, P., vom Berge, K., Matusik, W., Ramamoorthi, R., Lawrence, J., Rusinkiewicz, S., Dutré, P.: A compact factored representation of heterogeneous subsurface scattering. ACM Trans. Graph. 25(3), 746–753 (2006)CrossRef
  42.Prahl, S., van Gemert, M., Welch, A.: Determining the optical properties of turbid media by using the adding-doubling method. Appl. Opt. 32(4), 559–568 (1993)CrossRef
  43.Reddy, D., Ramamoorthi, R., Curless, B.: Frequency-space decomposition and acquisition of light transport under spatially varying illumination. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VI. LNCS, vol. 7577, pp. 596–610. Springer, Heidelberg (2012)
  44.Veach, E.: Robust Monte Carlo methods for light transport simulation. Ph.D. thesis, Stanford University (1997)
  45.Velten, A., Wu, D., Jarabo, A., Masia, B., Barsi, C., Joshi, C., Lawson, E., Bawendi, M., Gutierrez, D., Raskar, R.: Femto-photography: capturing and visualizing the propagation of light. ACM Trans. Graph. 32(4), 44:1–44:8 (2013)CrossRef
  46.Wang, J., Zhao, S., Tong, X., Lin, S., Lin, Z., Dong, Y., Guo, B., Shum, H.: Modeling and rendering of heterogeneous translucent materials using the diffusion equation. ACM Trans. Graph. 27(1) (2008)
  47.Wu, D., Velten, A., OToole, M., Masia, B., Agrawal, A., Dai, Q., Raskar, R.: Decomposing global light transport using time of flight imaging. ACM Trans. Graph. 107(2), 123–138 (2014)MathSciNet
  48.Wu, D., Wetzstein, G., Barsi, C., Willwacher, T., Dai, Q., Raskar, R.: Ultra-fast lensless computational imaging through 5D frequency analysis of time-resolved light transport. ACM Trans. Graph. 110(2), 128–140 (2014)
  49.Wyman, D., Patterson, M., Wilson, B.: Similarity relations for the interaction parameters in radiation transport. Appl. Opt. 28(24), 5243–5249 (1989)CrossRef
  50.Zeiler, M.D.: Adadelta: an adaptive learning rate method. arXiv preprint arXiv:​1212.​5701 (2012)
  51.Zhao, S., Ramamoorthi, R., Bala, K.: High-order similarity relations in radiative transfer. ACM Trans. Graph. (2014)
  
• 作者单位：Ioannis Gkioulekas (17)
  Anat Levin (18)
  Todd Zickler (17)
  
  17. Harvard University, Cambridge, USA
  18. Weizmann Institute of Science, Rehovot, Israel
  
• 丛书名：Computer Vision ¨C ECCV 2016
• ISBN：978-3-319-46487-9
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9907

文摘

Inferring internal scattering parameters for general, heterogeneous materials, remains a challenging inverse problem. Its difficulty arises from the complex way in which scattering materials interact with light, as well as the very high dimensionality of the material space implied by heterogeneity. The recent emergence of diverse computational imaging techniques, together with the widespread availability of computing power, present a renewed opportunity for tackling this problem. We take first steps in this direction, by deriving theoretical results, developing an algorithmic framework, and performing quantitative evaluations for the problem of heterogeneous inverse scattering from simulated measurements of different computational imaging configurations.