Abstract
With the rapid development of 3D devices, the depth map extraction method of 2D to 3D conversion has become a research hot spot in the field of computer vision. In this paper, on account of collected literatures and documents, we mainly introduces two methods of automatic depth map extraction based respectively on depth clues and machine learning. The depth map extraction method based on clues of several implementation algorithms is introduced, and its respective advantages and disadvantages are summarized. While for the depth map extraction method based on machine learning, we show the process of depth map extraction as an example. Moreover the parametric method and the non-parametric method are compared, and their respective advantages and disadvantages are pointed out. Finally we summarize the improved depth map extraction algorithm in the recent years, and the technical prospect is also discussed.
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Herrera, J.L., Del-Blanco, C.R., García, N.: Automatic depth extraction from 2D images using a cluster-based learning framework. IEEE Trans. Image Process. 319, 3288–3299 (2018)
Cozman, F., Krotkov, E.: Depth from scattering. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 801–806. IEEE Press, San Juan (1997)
Choe, Y., Kashyap, R.L.: Shape from textured and shaded surface. In: 10th International Conference on Pattern Recognition, pp. 294–296. IEEE Press, Atlantic City (1990)
Battiato, S., Curti, S., Cascia, M.L., Tortora, M., Scordato, E.: Depth map generation by image classification. In: SPIE International Society for Optical Engineering. San Jose, CA, pp. 95–104 (2004)
Han, K., Hong, K.: Geometric and texture cue based depth-map estimation for 2D to 3D image conversion. In: IEEE International Conference on Consumer Electronics, pp. 651–652. IEEE Press, ChiangMai (2011)
Ji, P., Wang, L., Li, D., Zhang, M.: An automatic 2D to 3D conversion algorithm using multi-depth cues. In: Proceedings of International Conference on Audio, Language and Image Processing, pp. 546–550. IEEE Press, Shanghai (2012)
Wafa, A., Nasiopoulos, P., Leung, V.C., Pourazad, M.T.: Automatic real-time 2D-to-3D conversion for scenic views. In: 7th International Workshop on Quality of Multimedia Experience, pp. 1–5. IEEE Press, Costa Navarino (2015)
Levin, A., Lischinski, D., Weiss, Y.: A closed-form solution to natural image matting. IEEE Trans. Pattern Anal. Mach. Intell. 350, 228–242 (2008)
Chang, Y.-L., Chen, W.-Y., Chang, J.-Y., Tsai, Y.-M., Lee, C.-L., Chen, L.-G.: Priority depth fusion for the 2D to 3D conversion system. In: Proceedings of SPIE 3D Image Capture Applications, p. 680513 (2008)
Harman, P.V., Flack, J., Fox, S.: Rapid 2D-to-3D conversion. In: Proceeding of SPIE, pp. 78–86. Society of Photo-Optical Instrumentation Engineers Press, San Jose (2002)
Saxena, A., Chung, S.H., Ng, A.Y.: Learning depth from single monocular images. In: International Conference on Neural Information Processing Systems, Taiwan (2005)
Konrad, J., Wang, M., Ishwar, P.: 2D-to-3D image conversion by learning depth from examples. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 16–22. IEEE Press, Providence (2012)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 886–893. IEEE Press, San Diego (2005)
Herrera, J.L., del-Blanco, C.R., García, N.: A novel 2D to 3D video conversion system based on a machine learning approach. IEEE Trans. Consum. Electron. 736, 429–436 (2016)
Liu, Y., Lin, X., Zhang, Q., Izquierdo, E.: Improved indoor scene geometry recognition from single image based on depth map. In: Proceedings of Image, Video, and Multidimensional Signal Processing, pp. 1–4. IEEE Press, Seoul (2013)
Yuan, H.X., Wu, S.Q., Yu, H.Q.: Semantic-level depth migration 2D to 3D algorithm. J. Comput.-Aided Des. Comput. Graph. 301, 72–80 (2014)
Xu, H., Jiang, M., Li, F.: Depth estimation algorithm based on data-driven approach and depth cues for stereo conversion in three-dimensional displays. Opt. Eng. 55, 123106 (2016)
Yao, G.S., Sun, S.Y., Fang, J.N.: Depth estimation of night unmanned vehicle scene based on infrared and radar. Laser Optoelectron. Progress. 312, 158–164 (2017)
Cai, B., Xu, X., Jia, K., Qing, C., Tao, D.: DehazeNet: an end-to-end system for single image haze removal. IEEE Trans. Image Process. 299, 5187–5198 (2016)
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Cheng, Y., Dong, Y., Tan, J. (2019). The Overview of 2D to 3D Automatic Conversion. In: Wang, Y., Huang, Q., Peng, Y. (eds) Image and Graphics Technologies and Applications. IGTA 2019. Communications in Computer and Information Science, vol 1043. Springer, Singapore. https://doi.org/10.1007/978-981-13-9917-6_12
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DOI: https://doi.org/10.1007/978-981-13-9917-6_12
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