Abstract
This paper describes a method for recognizing and tracking 3D objects in a single camera image and for determining their 3D poses. A model is trained solely based on the geometry information of a 3D CAD model of the object. We do not rely on texture or reflectance information of the object’s surface, making this approach useful for a wide range of object types and complementary to descriptor-based approaches.
An exhaustive search, which ensures that the globally best matches are always found, is combined with an efficient hierarchical search, a high percentage of which can be computed offline, making our method suitable even for time-critical applications. The method is especially suited for, but not limited to, the recognition and tracking of untextured objects like metal parts, which are often used in industrial environments.
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
Bay, H., Tuytelaars, T., Gool, L.V.: SURF: Speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Berlin (2006)
Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(4), 509–522 (2002)
Berg, A.C., Berg, T.L., Malik, J.: Shape matching and object recognition using low distortion correspondences. In: Computer Vision and Pattern Recognition, pp. 26–33 (2005)
Borotschnig, H., Paletta, L., Prantl, M., Prinz, A.: Appearance based active object recognition. Image and Vision Computing 18(9), 715–727 (2000)
Byne, J.H.M., Anderson, J.A.D.W.: A CAD based computer vision system. Image and Vision Computing 16(8), 533–539 (1998)
Costa, M.S., Shapiro, L.G.: 3D object recognition and pose with relational indexing. Computer Vision and Image Understanding 79(3), 364–407 (2000)
Cyr, C.M., Kimia, B.B.: 3D object recognition using shape similarity-based aspect graph. In: 8th International Conference on Computer Vision, vol. I, pp. 254–261 (2001)
David, P., DeMenthon, D.: Object recognition in high clutter images using line features. In: 10th International Conference on Computer Vision, pp. 1581–1588 (2005)
Di Zenzo, S.: A note on the gradient of a multi-image. Computer Vision, Graphics, and Image Processing 33, 116–125 (1986)
Drummond, T., Cipolla, R.: Real-time visual tracking of complex structures. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 932–946 (2002)
Hinterstoisser, S., Benhimane, S., Navab, N.: N3M: Natural 3D markers for real-time object detection and pose estimation. In: 11th International Conference on Computer Vision (2007)
Kollnig, H., Nagel, H.H.: 3d pose estimation by directly matching polyhedral models to gray value gradients. International Journal of Computer Vision 23(3), 283–302 (1997)
Ladikos, A., Benhimane, S., Navab, N.: A real-time tracking system combining template-based and feature-based approaches. In: International Conference on Computer Vision Theory and Applications (2007)
Liebelt, J., Schertler, K.: Precise registration of 3d models to images by swarming particles. In: Computer Vision and Pattern Recognition, pp. 1–8 (2007)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Marchand, É., Bouthemy, P., Chaumette, F.: A 2d-3d model-based approach to real-time visual tracking. Image Vision Comput. 19(13), 941–955 (2001)
Paterson, M.S., Yao, F.F.: Efficient binary space partitions for hidden-surface removal and solid modeling. Discrete & Computational Geometry 5(1), 485–503 (1990)
Pilet, J., Lepetit, V., Fua, P.: Real-time non-rigid surface detection. In: Computer Vision and Pattern Recognition, pp. 822–828 (2005)
Steger, C.: Occlusion, clutter, and illumination invariant object recognition. In: International Archives of Photogrammetry and Remote Sensing, vol. XXXIV, part 3A, pp. 345–350 (2002)
Ulrich, M.: Hierarchical Real-Time Recognition of Compound Objects in Images. PhD thesis, Bauingenieur- und Vermessungswesen der Technischen Universität München (2003)
Von Bank, C., Gavrila, D.M., Wöhler, C.: A visual quality inspection system based on a hierarchical 3d pose estimation algorithm. In: Michaelis, B., Krell, G. (eds.) DAGM 2003. LNCS, vol. 2781, pp. 179–186. Springer, Heidelberg (2003)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wiedemann, C., Ulrich, M., Steger, C. (2008). Recognition and Tracking of 3D Objects. In: Rigoll, G. (eds) Pattern Recognition. DAGM 2008. Lecture Notes in Computer Science, vol 5096. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69321-5_14
Download citation
DOI: https://doi.org/10.1007/978-3-540-69321-5_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69320-8
Online ISBN: 978-3-540-69321-5
eBook Packages: Computer ScienceComputer Science (R0)