Abstract
Dense depth maps can be estimated in a Bayesian sense from multiple calibrated still images of a rigid scene relative to a reference view [1]. This well-established probabilistic framework is extended by adaptively refining a triangular meshing procedure and by automatic cross-validation of model parameters. The adaptive refinement strategy locally adjusts the triangular meshing according to the measured image data. The new method substantially outperforms the competing techniques both in terms of robustness and accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Strecha, C., Fransens, R., van Gool, L.: Wide-baseline stereo from multiple views: a probabilistic account. In: Conference on Computer Vision and Pattern Recognition (CVPR 2004), vol. 1, pp. 552–559. IEEE Computer Society Press, Los Alamitos (2004)
Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision 47(1), 7–42 (2002)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Gargallo, P., Sturm, P.: Bayesian 3D modeling from images using multiple depth maps. In: Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 885–891. IEEE, Los Alamitos (2005)
Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2004)
Faugeras, O.: Three-Dimensional Computer Vision. MIT Press, Cambridge (1993)
Hermes, L., Buhmann, J.M.: A minimum entropy approach to adaptive image polygonization. IEEE Transactions on Image Processing 12(10), 1243–1258 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wey, P., Fischer, B., Bay, H., Buhmann, J.M. (2006). Dense Stereo by Triangular Meshing and Cross Validation. In: Franke, K., Müller, KR., Nickolay, B., Schäfer, R. (eds) Pattern Recognition. DAGM 2006. Lecture Notes in Computer Science, vol 4174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11861898_71
Download citation
DOI: https://doi.org/10.1007/11861898_71
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44412-1
Online ISBN: 978-3-540-44414-5
eBook Packages: Computer ScienceComputer Science (R0)