5/8/2023 0 Comments Translucent objects![]() ![]() 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. Water, a piece of glass, a piece of clear plastic are all examples of transparent objects.This paper presents a photometric stereo method that works for optically thick translucent objects exhibiting subsurface scattering. Objects that allow light to pass through them completely are called Transparent objects. ![]() Opaque objects What are Transparent objects? Wood, metal sheets, coloured plastic are some examples of an opaque object. Objects that do not allow light to pass through them are called Opaque objects. They can be classified under three categories – Objects that do not emit light are called Non-luminous objects. The sun, a tube light, a torch are all luminous objects. What is a source of light called?īodies or objects that emit light are known as Luminous objects. Which means no light can bounce off the objects and reach our eyes. In a dark room, there is no source of light. Why can’t we see anything in a dark room? This is why we can see the objects around us. Light from a source of light falls on an object and bounces to our eyes. Light is a form of energy that helps us perceive objects in our surroundings.
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