Abstract
Pose estimation is an important step in the grasping of workpieces. However, most previous works aim to use the 3D vision system to locate the 3D pose of the object. This paper develops a pose estimation of 3D object with 2D vision system. The proposed method includes two steps: (a) a hierarchy model of 2D views of the object is firstly constructed off-line; (b) the pose of object is then estimated by measuring the similarity of the model and target image. The proposed method is inherently robust against noise and illumination changes, and also efficient in real applications.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Pretto, A., Tonello, S., Menegatti, E.: Flexible 3D localization of planar objects for industrial bin-picking with monocamera vision system. In: 9th IEEE International Conference on Automation Science and Engineering (CASE), Madison, pp. 168–175. IEEE Press (2013)
Hinterstoisser, S., et al.: Gradient response maps for real-time detection of textureless objects. In: 32nd IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, pp. 876–888. IEEE Press (2012)
Hinterstoisser, S., et al.: Model based training, detection and pose estimation of texture-less 3D objects in heavily cluttered scenes. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012. LNCS, vol. 7724, pp. 548–562. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37331-2_42
Rios-Cabrera, R., Tuytelaars, T.: Discriminatively trained templates for 3D object detection: a real time scalable approach. In: 14th IEEE International Conference on Computer Vision, Sydney, pp. 2048–2055. IEEE Press (2013)
Brachmann, E., Krull, A., Michel, F., Gumhold, S., Shotton, J., Rother, C.: Learning 6D object pose estimation using 3D object coordinates. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8690, pp. 536–551. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10605-2_35
Bonde, U., Badrinarayanan, V., Cipolla, R.: Robust instance recognition in presence of occlusion and clutter. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8690, pp. 520–535. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10605-2_34
Borgefors, G.: Hierarchical chamfer matching: a parametric edge matching algorithm. In: 8th IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 10, pp. 849–865. IEEE Press (1988)
Steger, C.: Similarity measures for occlusion, clutter, and illumination invariant object recognition. In: Radig, B., Florczyk, S. (eds.) DAGM 2001. LNCS, vol. 2191, pp. 148–154. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-45404-7_20
Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. In: 22nd IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, pp. 509–522. IEEE Press (2002)
Ulrich, M., Wiedemann, C., Steger, C.: Combining scale-space and similarity-based aspect graphs for fast 3D object recognition. In: 32nd IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, pp. 1902–1914. IEEE Press (2012)
Lowe, D.G.: Three-dimensional object recognition from single two dimensional images. Artif. Intell. 31, 355–395 (1987)
Lowe, D.G.: Fitting parametrized 3-D models to images. In: 11th IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, pp. 441–450. IEEE Press (1991)
Costa, M.S., Shapiro, L.G.: 3D object recognition and pose with relational indexing. Comput. Vis. Image Underst. 79, 364–407 (2000)
Wiedemann, C., Ulrich, M., Steger, C.: Recognition and tracking of 3D objects. In: Symposium on Pattern Recognition, vol. 5096, pp. 132–141 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Chen, B., Su, J., Lv, K., Xue, D. (2018). Estimation of 3-D Pose with 2-D Vision Based on Shape Matching Method. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11306. Springer, Cham. https://doi.org/10.1007/978-3-030-04224-0_20
Download citation
DOI: https://doi.org/10.1007/978-3-030-04224-0_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04223-3
Online ISBN: 978-3-030-04224-0
eBook Packages: Computer ScienceComputer Science (R0)