Abstract
We report a method for reconstructing a three-dimensional (3D) depth map using a single two-dimensional (2D) image. Our method is designed to reconstruct manmade objects, such as buildings. We first estimate the normal map, and then integrate it to obtain the depth map. To estimate the normal map, we analyze the co-occurrence relation between the normal vectors and image features in a training dataset. We consider the corners and lines to be the image features. The training dataset is formed of 3D game scenes. In the offline learning phase, we detect corners and lines in the normal map of each game scene using a detection algorithm, and observe the normal vectors around them. Then we construct a database of the co-occurrence relations, i.e., how frequently each corner or line appears with each normal vector. In the online reconstruction phase, given an input image, we detect the corners and lines using the same detection algorithm, and estimate the normal vectors around them based on the learned co-occurrence relation. We formulate this estimation using a Markov random field. Finally, the estimated normal map is integrated by solving Poisson’s equation, and we obtain a depth map.
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References
Shan Q, Adams R, Curless B, Furukawa Y, Seitz SM (2013) The visual turing test for scene reconstruction. In: Proceedings of 3DV13
Debevec PE, Taylor CJ, Malik J (1996) Modeling and rendering architecture from photographs: a hybrid geometry- and image-based approach. Comput Graph, Ann Conf Ser 30:11–20
Horry Y, Anjyo K, Arai K (1997) Tour into the picture: using a spidery mesh interface to make animation from a single image. In: Proceedings of SIGGRAPH ’97, pp 225–232
Li Z, Guillaume D-P, Jean-Sebastien S, SM Seitz (2001) Single view modeling of free-form scenes. In: Proceedings of CVPR 2001, pp 990–997
Hoiem D, Efros AA, Hebert M (2005) Automatic photo pop-up. ACM Trans. Graph. 24(3):577–584
Saxena A, Chung SH, Ng AY (2008) 3-D depth reconstruction from a single still image. Int. J. Comput. Vis. 76(1):53–69
UBISOFT: Anno 1404 (2010)
Szeliski R, Zabih R, Scharstein D, Veksler O, Kolmogorov V, Agarwala A, Tappen M, Rother C (2008) A comparative study of energy minimization methods for Markov random fields with smoothness-based priors. IEEE Trans. Pattern Anal. Mach. Intell. 30(6):1068–1080
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© 2014 Springer Japan
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Okabe, M., Anjyo, K., Onai, R. (2014). Single-View 3D Reconstruction by Learning 3D Game Scenes. In: Anjyo, K. (eds) Mathematical Progress in Expressive Image Synthesis I. Mathematics for Industry, vol 4. Springer, Tokyo. https://doi.org/10.1007/978-4-431-55007-5_19
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DOI: https://doi.org/10.1007/978-4-431-55007-5_19
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