Abstract
We compare two approaches to the problem of estimating the depth of a point in space from observing its image position in two different cameras: 1. The classical photogrammetric approach explicitly models the two cameras and estimates their intrinsic and extrinsic parameters using a tedious calibration procedure; 2. A generic machine learning approach where the mapping from image to spatial coordinates is directly approximated by a Gaussian Process regression. Our results show that the generic learning approach, in addition to simplifying the procedure of calibration, can lead to higher depth accuracies than classical calibration although no specific domain knowledge is used.
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References
Luhmann, T.: Nahbereichsphotogrammetrie - Grundlagen, Methoden und Anwendungen. Wichmann (2000) [in German]
Cristianini, N., Shawe-Taylor, J.: Support Vector Machines - and other kernel-based methods. Cambridge University Press, Cambridge (2000)
Kay, S.M.: Statistical Signal Processing, vol. I. Prentice-Hall, Englewood Cliffs (1993)
Sundararajan, S., Keerthi, S.S.: Predictive Approaches for Choosing Hyperparameters in Gaussian Processes. Neural Computation 13, 1103–1118 (2001)
Williams, C.K.I., Rasmussen, C.E.: Gaussian processes for regression. Advances in Neural Information Processing Systems 8, 514–520 (1996)
Sinz, F.: Kamerakalibrierung und Tiefenschätzung - Ein Vergleich von klassischer Bündelblockausgleichung und statistischen Lernalgorithmen (2004), http://www.kyb.tuebingen.mpg.de/~fabee [in German]
Abraham, S., Förstner, W.: Zur automatischen Modellwahl bei der Kalibrierung von CCD-Kameras. In: 19. DAGM-Symposium Mustererkennung 1997, pp. 147–155. Springer, Heidelberg (1997)
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Sinz, F.H., Candela, J.Q., Bakır, G.H., Rasmussen, C.E., Franz, M.O. (2004). Learning Depth from Stereo. In: Rasmussen, C.E., Bülthoff, H.H., Schölkopf, B., Giese, M.A. (eds) Pattern Recognition. DAGM 2004. Lecture Notes in Computer Science, vol 3175. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28649-3_30
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DOI: https://doi.org/10.1007/978-3-540-28649-3_30
Publisher Name: Springer, Berlin, Heidelberg
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