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Depth Estimation

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Computer Vision

Synonyms

2.5D estimation; Depth imaging, distance estimation and three dimensional estimation; Distance estimation; Three dimensional estimation

Related Concepts

Definition

Depth estimation describes the process of measuring or estimating distances from sensor data, typically in a 2D array of depth range data. The sensors may be either optical camera configurations (monocular, stereo, or multiview stereo camera rigs), active projector-camera configurations, or active range cameras.

Background

Depth estimation is one of the fundamental computer vision tasks, as it involves the inverse problem of reconstructing the three-dimensional scene structure from two-dimensional projections. Given a 2D image of a 3D scene, the goal of depth estimation is to recover, for every image pixel, the distance from the camera center to the nearest...

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References

  1. Boyer K, Kak A (1987) Color-encoded structured light for rapid active ranging. IEEE Trans Pattern Anal Mach Intell 9(1)

    Google Scholar 

  2. Boykov Y, Kolmogorov V (2004) An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans Pattern Anal Mach Intell 26(9):1124–1137

    Article  Google Scholar 

  3. Collins RT (1996) A space-sweep approach to true multi-image matching. In: Proceedings international conference on computer vision and pattern recognition, pp 358–363

    Google Scholar 

  4. Eigen D, Puhrsch C, Fergus R (2014) Depth map prediction from a single image using a multi-scale deep network. In: Advances in neural information processing systems, pp 2366–2374

    Google Scholar 

  5. Fu H, Gong M, Wang C, Batmanghelich K, Tao D (2018) Deep ordinal regression network for monocular depth estimation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2002–2011

    Google Scholar 

  6. Garg R, Vijay Kumar BG, Carneiro G, Reid I (2016) Unsupervised CNN for single view depth estimation: geometry to the rescue. In: European conference on computer vision, pp 740–756. Springer

    Google Scholar 

  7. Geiger A, Lenz P, Stiller C, Urtasun R (2013) Vision meets robotics: the kitti dataset. Int J Robot Res 32(11):1231–1237

    Article  Google Scholar 

  8. Godard C, Mac Aodha O, Brostow GJ (2017) Unsupervised monocular depth estimation with left-right consistency. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 270–279

    Google Scholar 

  9. Gong M, Yang R, Wang L, Gong M (2007) A performance study on different cost aggregation approaches used in real-time stereo matching. Int J Comput Vis 75(2):283–296

    Article  Google Scholar 

  10. Hartley R, Zisserman A (2003) Multiple view geometry in computer vision. Cambridge University Press. ISBN:0-521-54051-8

    MATH  Google Scholar 

  11. Hirschmueller H (2008) Stereo processing by semi-global matching and mutual information. IEEE Trans Pattern Anal Mach Intell 30(2):328–341

    Article  Google Scholar 

  12. Kolb A, Barth E, Koch R, Larsen R (2010) Time-of-flight cameras in computer graphics. Comput Graph Forum 29(1):141–159

    Article  Google Scholar 

  13. Laina I, Rupprecht C, Belagiannis V, Tombari F, Navab N (2016) Deeper depth prediction with fully convolutional residual networks. In: 2016 fourth international conference on 3D vision (3DV), pp 239–248. IEEE

    Google Scholar 

  14. Liu F, Shen C, Lin G, Reid I (2016) Learning depth from single monocular images using deep convolutional neural fields. IEEE Trans Pattern Anal Mach Intell 38(10):2024–2039

    Article  Google Scholar 

  15. Nayar SK, Watanabe M, Noguchi M (1996) Real-time focus range sensor. IEEE Trans Pattern Anal Mach Intell 18(12):1186–1198

    Article  Google Scholar 

  16. Pollefeys M, Koch R, Van Gool L (1999) A simple and efficient rectification method for general motion. In: Proceedings of 7th international conference on computer vision, vol 1, pp 496–501

    Google Scholar 

  17. Saxena A, Chung SH, Ng AY (2006) Learning depth from single monocular images. In: Advances in neural information processing systems, pp 1161–1168

    Google Scholar 

  18. Scharstein D, Szeliski R (2002) A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int J Comput Vis 47:7–42

    Article  Google Scholar 

  19. Schwarte R, Xu Z, Heinol H-G, Olk J, Klein R, Buxbaum B, Fischer H, Schulte J (1997) New electro-optical mixing and correlating sensor: facilities and applications of the photonic mixer device (PMD). In: Proceedings of SPIE 3100

    Google Scholar 

  20. Shade J, Gortler SJ, He LW, Szeliski R (1998) Layered depth images. In: SIGGRAPH, proceedings of the 25th annual conference on computer graphics and interactive techniques, pp 231–242

    Google Scholar 

  21. Szeliski R (2010) Computer vision, algorithms and applications. Springer, London

    MATH  Google Scholar 

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Correspondence to Reinhard Koch .

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Koch, R., Bruenger, J. (2021). Depth Estimation. In: Computer Vision. Springer, Cham. https://doi.org/10.1007/978-3-030-03243-2_125-1

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  • DOI: https://doi.org/10.1007/978-3-030-03243-2_125-1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03243-2

  • Online ISBN: 978-3-030-03243-2

  • eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering

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