Abstract
In this paper, we consider scenes that are immersed in transparent refractive media with a dynamic surface. We take the first steps to reconstruct both the 3D fluid surface shape and the 3D structure of immersed scene simultaneously by utilizing distortion and defocus clues. We demonstrate that the images captured through a refractive dynamic fluid surface are the distorted and blurred versions of all-in-focused (AIF) images captured through a flat fluid surface. The amounts of distortion and refractive blur are formulated by the shape of fluid surface, scene depth and camera parameters, based on our refractive geometry model of a finite aperture imaging system. An iterative optimization algorithm is proposed to reconstruct the distortion and immersed scene depth, which are then used to infer the 3D fluid surface. We validate and demonstrate the effectiveness of our approach on a variety of synthetic and real scenes under different fluid surfaces.
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Agrawal, A., Chellappa, R., Raskar, R.: An algebraic approach to surface reconstruction from gradient fields. In: ICCV, vol. 1, pp. 174–181. IEEE (2005)
Agrawal, A., Raskar, R., Chellappa, R.: What is the range of surface reconstructions from a gradient field? In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 578–591. Springer, Heidelberg (2006)
Alterman, M., Schechner, Y., Perona, P., Shamir, J.: Detecting motion through dynamic refraction. PAMI 35(1), 245–251 (2013)
Alterman, M., Schechner, Y.Y., Swirski, Y.: Triangulation in random refractive distortions. In: ICCP, pp. 1–10. IEEE (2013)
Ben-Ezra, M., Nayar, S.K.: What does motion reveal about transparency? In: ICCV, pp. 1025–1032. IEEE (2003)
Brox, T., Bregler, C., Malik, J.: Large displacement optical flow. In: CVPR, pp. 41–48. IEEE (2009)
Brox, T., Bruhn, A., Papenberg, N., Weickert, J.: High accuracy optical flow estimation based on a theory for warping. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3024, pp. 25–36. Springer, Heidelberg (2004)
Chang, Y.-J., Chen, T.: Multi-view 3d reconstruction for scenes under the refractive plane with known vertical direction. In: ICCV, pp. 351–358. IEEE (2011)
Ding, Y., Li, F., Ji, Y., Yu, J.: Dynamic fluid surface acquisition using a camera array. In: ICCV, pp. 2478–2485. IEEE (2011)
Donate, A., Ribeiro, E.: Improved reconstruction of images distorted by water waves. In: Advances in Computer Graphics and Computer Vision, pp. 264–277. Springer, Heidelberg (2007)
Efros, A., Isler, V., Shi, J., Visontai, M.: Seeing through water. In: NIPS, vol. 17, pp. 393–400 (2005)
Favaro, P.: Recovering thin structures via nonlocal-means regularization with application to depth from defocus. In: CVPR, pp. 1133–1140. IEEE (2010)
Favaro, P., Soatto, S.: 3D shape reconstruction and image restoration: exploiting defocus and motion blur. Springer Verlag (2006)
Ferreira, R., Costeira, J.P., Santos, J.A.: Stereo reconstruction of a submerged scene. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds.) IbPRIA 2005. LNCS, vol. 3522, pp. 102–109. Springer, Heidelberg (2005)
Gupta, M., Narasimhan, S.G., Schechner, Y.Y.: On controlling light transport in poor visibility environments. In: CVPR, pp. 1–8. IEEE (2008)
Hirschmuller, H., Scharstein, D.: Evaluation of cost functions for stereo matching. In: CVPR, pp. 1–8. IEEE (2007)
Huynh, C.P., Robles-Kelly, A., Hancock, E.: Shape and refractive index recovery from single-view polarisation images. In: CVPR, pp. 1229–1236. IEEE (2010)
Ihrke, I., Goidluecke, B., Magnor, M.: Reconstructing the geometry of flowing water. In: ICCV, vol. 2, pp. 1055–1060. IEEE (2005)
Jähne, B., Klinke, J., Waas, S.: Imaging of short ocean wind waves: a critical theoretical review. JOSA A 11(8), 2197–2209 (1994)
Kidger, M.J.: Fundamental optical design, vol. 92. SPIE Press Bellingham, Washington, DC (2002)
Lin, X., Suo, J., Cao, X., Dai, Q.: Iterative feedback estimation of depth and radiance from defocused images. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012, Part IV. LNCS, vol. 7727, pp. 95–109. Springer, Heidelberg (2013)
Morris, N.J., Kutulakos, K.N.: Dynamic refraction stereo. In: ICCV, vol. 2, pp. 1573–1580. IEEE (2005)
Morris, N.J., Kutulakos, K.N.: Reconstructing the surface of inhomogeneous transparent scenes by scatter-trace photography. In: ICCV, pp. 1–8. IEEE (2007)
Murase, H.: Surface shape reconstruction of an undulating transparent object. In: ICCV, pp. 313–317. IEEE (1990)
Narasimhan, S.G., Nayar, S.K., Sun, B., Koppal, S.J.: Structured light in scattering media. In: ICCV, vol. 1, pp. 420–427. IEEE ( (2005)
Oreifej, O., Shu, G., Pace, T., Shah, M.: A two-stage reconstruction approach for seeing through water. In: CVPR, pp. 1153–1160. IEEE (2011)
Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. IJCV 47(1-3), 7–42 (2002)
Schechner, Y.Y., Karpel, N.: Recovery of underwater visibility and structure by polarization analysis. IEEE Journal of Oceanic Engineering 30(3), 570–587 (2005)
Schechner, Y.Y., Kiryati, N.: Depth from defocus vs. stereo: How different really are they? IJCV 39(2), 141–162 (2000)
Tian, Y., Narasimhan, S.G.: Seeing through water: Image restoration using model-based tracking. In: ICCV, pp. 2303–2310. IEEE (2009)
Tian, Y., Narasimhan, S.G.: A globally optimal data-driven approach for image distortion estimation. In: CVPR, pp. 1277–1284. IEEE (2010)
Treibitz, T., Schechner, Y., Kunz, C., Singh, H.: Flat refractive geometry. PAMI 34(1), 51–65 (2012)
Wen, Z., Lambert, A., Fraser, D., Li, H.: Bispectral analysis and recovery of images distorted by a moving water surface. Applied Optics 49(33), 6376–6384 (2010)
Wetzstein, G., Roodnick, D., Heidrich, W., Raskar, R.: Refractive shape from light field distortion. In: ICCV, pp. 1180–1186. IEEE (2011)
Yau, T., Gong, M., Yang, Y.-H.: Underwater camera calibration using wavelength triangulation. In: CVPR, pp. 2499–2506. IEEE (2013)
Ye, J., Ji, Y., Li, F., Yu, J.: Angular domain reconstruction of dynamic 3d fluid surfaces. In: CVPR, pp. 310–317. IEEE (2012)
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Zhang, M., Lin, X., Gupta, M., Suo, J., Dai, Q. (2014). Recovering Scene Geometry under Wavy Fluid via Distortion and Defocus Analysis. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds) Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8693. Springer, Cham. https://doi.org/10.1007/978-3-319-10602-1_16
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DOI: https://doi.org/10.1007/978-3-319-10602-1_16
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