ACM SIGGRAPH Sketches and Applications (2003) ACM Transactions on Graphics (TOG) 30 (2011)īorshukov, G., Lewis, J.: Realistic human face rendering for “the matrix reloaded”. 511–518 (2001)ĭ’Eon, E., Irving, G.: A quantized-diffusion model for rendering translucent materials. Jensen, H.W., Marschner, S.R., Levoy, M., Hanrahan, P.: A practical model for subsurface light transport. International Journal of Computer Vision (IJCV) 102, 33–55 (2012)ĭonner, C., Lawrence, J., Ramamoorthi, R., Hachisuka, T., Jensen, H.W., Nayar, S.: An empirical BSSRDF model. Gupta, M., Agrawal, A., Veeraraghavan, A., Narasimhan, S.G.: A Practical Approach to 3D Scanning in the Presence of Interreflections, Subsurface Scattering and Defocus. ![]() of International Conference on Computer Vision (ICCV) (2011) Gu, J., Kabayashi, T., Gupta, M., Nayar, S.K.: Multiplexed Illumination for Scene Recovery in the Presence of Global Illumination. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2008) of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2007)Ĭhen, T., Seidel, H.P., Lensch, H.P.A.: Modulated phase-shifting for 3D scanning. of International Conference on Computer Vision (ICCV) (1990)Ĭhen, T., Lensch, H.P.A., Fuchs, C., Seidel, H.P.: Polarization and Phase-shifting for 3D Scanning of Translucent Objects. Nayar, S.K., Ikeuchi, K., Kanade, T.: Shape from interreflections. Pattern Analysis and Machine Intelligence (PAMI) 32, 1060–1071 (2010) ![]() Goldman, D.B., Curless, B., Hertzmann, A., Seitz, S.M.: Shape and spatially-varying BRDFs from photometric stereo. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2014)Īlldrin, N., Zickler, T., Kriegman, D.: Photometric stereo with non-parametric and spatially-varying reflectance. Computer Graphics Forum 30(8), 2279–2287 (2011)ĭong, B., Moore, K., Zhang, W., Peers, P.: Scattering Parameters and Surface Normals from Homogeneous Translucent Materials using Photometric Stereo. ![]() Munoz, A., Echevarria, J.I., Seron, F.J., Gutierrez, D.: Convolution-based simulation of homogeneous subsurface scattering. Nayar, S.K., Krishnan, G., Grossberg, M.D., Raskar, R.: Fast separation of direct and global components of a scene using high frequency illumination. Moore, K.D., Peers, P.: An empirical study on the effects of translucency on photometric stereo. Wu, L., Ganesh, A., Shi, B., Matsushita, Y., Wang, Y., Ma, Y.: Robust Photometric Stereo via Low-Rank Matrix Completion and Recovery. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2012) Ikehata, S., Wipf, D., Matsushita, Y., Aizawa, K.: Robust Photometric Stereo using Sparse Regression. Lambert, J.H.: Photometria sive de mensure de gratibus luminis. Woodham, R.J.: Photometric Method For Determining Surface Orientation From Multiple Images. This process is experimental and the keywords may be updated as the learning algorithm improves. These keywords were added by machine and not by the authors. Experimental results of both synthetic and real-world scenes show the effectiveness of the proposed method. Based on this observation, we cast the photometric stereo problem for optically thick translucent objects as a deconvolution problem, and develop a method to recover accurate surface normals. ![]() We extend this observation and show that the original surface normal convolved with the scattering kernel corresponds to the blurred surface normal that can be obtained by a conventional photometric stereo technique. Our method is built upon the previous studies showing that subsurface scattering is approximated as convolution with a blurring kernel. This paper presents a photometric stereo method that works for optically thick translucent objects exhibiting subsurface scattering.
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