Seite - 157 - in Proceedings of the OAGM&ARW Joint Workshop - Vision, Automation and Robotics

[4] Bennett Wilburn, Neel Joshi, Vaibhav Vaish, Eino-Ville Talvala, Emilio Antunez, Adam Barth, Andrew Adams, Mark Horowitz, and Marc Levoy. High performance imaging using large camera arrays. ACMTrans. Graph., 24(3):765–776, July 2005. [5] Svorad Sˇtolc, Reinhold Huber-Mo¨rk, Branislav Holla¨nder, and Daniel Soukup. Depth and all-in-focus images obtained by multi-line-scan light-fieldapproach. In IS&T/SPIEElectronic Imaging, pages902407– 902407. International Society for Optics and Photonics, 2014. [6] Jonathan S Golan. Moore–penrose pseudoinverses. In The Linear Algebra a Beginning Graduate Student Ought to Know, pages 441– 452. Springer, 2012. [7] Martı´n Abadi et al. TensorFlow: Large-scale machine learning on heterogeneous systems, 2015. Software available from tensorflow.org. [8] Diego Nehab, Szymon Rusinkiewicz, James Davis, and Ravi Ra- mamoorthi. Efficiently combining positions and normals for precise 3d geometry. In ACM transactions on graphics (TOG), volume 24, pages 536–543. ACM, 2005. [9] Robert C Bolles, H Harlyn Baker, and David H Marimont. Epipolar- plane image analysis: An approach to determining structure from motion. International Journal of Computer Vision, 1(1):7–55, 1987. [10] Michael W Tao, Pratul P Srinivasan, Jitendra Malik, Szymon Rusinkiewicz, and Ravi Ramamoorthi. Depth from shading, defocus, and correspondence using light-field angular coherence. In Pro- ceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1940–1948, 2015. [11] Hideki Hayakawa. Photometric stereo under a light source with arbitrary motion. JOSAA, 11(11):3079–3089, 1994. [12] KV Rajaram, Guturu Parthasarathy, and MA Faruqi. A neural network approach to photometric stereo inversion of real-world reflectance maps for extracting 3-d shapes of objects. IEEE transactions on systems, man, and cybernetics, 25(9):1289–1300, 1995. [13] Wen-Chang Cheng. Neural-network-based photometric stereo for 3d surface reconstruction. In Neural Networks, 2006. IJCNN’06. International Joint Conference on, pages 404–410. IEEE, 2006. [14] Siu-Yeung Cho and Tommy WS Chow. Shape recovery from shading by a new neural-based reflectance model. IEEE Transactions on Neural Networks, 10(6):1536–1541, 1999. [15] Berthold KP Horn and Michael J Brooks. Shape from shading. MIT press, 1989. [16] Blender Online Community. Blender - a 3Dmodelling and rendering package. Blender Foundation, Blender Institute, Amsterdam, [17] Franois Gastaldo. How to make your own physically correct shading. Blender Converence, 2012. [18] Michael Oren and Shree K Nayar. Generalization of lambert’s reflectance model. In Proceedings of the 21st annual conference on Computer graphics and interactive techniques, pages 239–246. ACM, 1994. [19] Bruce Walter, Stephen R. Marschner, Hongsong Li, and Kenneth E. Torrance. Microfacet models for refraction through rough surfaces. In Proceedings of the 18th Eurographics Conference on Rendering Techniques, EGSR’07, pages 195–206, Aire-la-Ville, Switzerland, Switzerland, 2007. Eurographics Association. [20] R. Basri and D. W. Jacobs. Lambertian reflectance and linear subspaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(2):218–233, Feb 2003. [21] F John Reh. Pareto’s principle-the 80-20 rule. BUSINESS CREDIT- NEWYORKTHENCOLUMBIAMD-, 107(7):76, 2005. [22] Igiri Chinwe Peace, Anyama Oscar Uzoma, and Silas Abasiama Ita. Effect of learning rate on artificial neural network in machine learning. In International Journal of Engineering Research and Technology, volume 4. IJERT, 2015. [23] Rob J Hyndman and Anne B Koehler. Another look at measures of forecast accuracy. International journalof forecasting, 22(4):679–688, 2006. 157