Abstract
This paper introduces a robust and efficient vision based method for object detection and 3D pose estimation that exploits a novel edge-based registration algorithm we called Direct Directional Chamfer Optimization (D\(^{2}\)CO). Our approach is able to handle textureless and partially occluded objects and does not require any off-line object learning step. Depth edges and visible patterns extracted from the 3D CAD model of the object are matched against edges detected in the current grey level image by means of a 3D distance transform represented by an image tensor, that encodes the minimum distance to an edge point in a joint direction/location space. D\(^{2}\)CO refines the object position employing a non-linear optimization procedure, where the cost being minimized is extracted directly from the 3D image tensor. Differently from other popular registration algorithms as ICP, that require to constantly update the correspondences between points, our approach does not require any iterative re-association step: the data association is implicitly optimized while inferring the object position. This enables D\(^{2}\)CO to obtain a considerable gain in speed over other registration algorithms while presenting a wider basin of convergence. We tested our system with a set of challenging untextured objects in presence of occlusions and cluttered background, showing accurate results and often outperforming other state-of-the-art methods.
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Notes
- 1.
In our case, a distance transform is an image where each pixel reports the distance to the closest edge pixel (edgel).
- 2.
The dataset is available at http://www.dis.uniroma1.it/~labrococo/D2CO.
- 3.
The objects used in the experiments are currently employed in the RoCKIn@Work competitions, http://rockinrobotchallenge.eu.
References
Barrow, H.G., Tenenbaum, J.M., Bolles, R.C., Wolf, H.C.: Parametric correspondence and chamfer matching: two new techniques for image matching. In: Proceedings of 5th International Joint Conference on Artificial Intelligence, pp. 659–663 (1977)
Besl, P.J., McKay, N.D.: A method for registration of 3-d shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239–256 (1992)
Borgefors, G.: Hierarchical chamfer matching: a parametric edge matching algorithm. IEEE Trans. Pattern Anal. Mach. Intell. 10(6), 849–865 (1988)
Cai, H., Werner, T., Matas, J.: Fast detection of multiple textureless 3-D objects. In: Chen, M., Leibe, B., Neumann, B. (eds.) ICVS 2013. LNCS, vol. 7963, pp. 103–112. Springer, Heidelberg (2013)
Choi, C., Christensen, H.I.: 3d textureless object detection and tracking: an edge-based approach. In: Proceedings of: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3877–3884 (2012)
Damen, D., Bunnun, P., Calway, A., Mayol-Cuevas, W.W.: Real-time learning and detection of 3d texture-less objects: a scalable approach. In: Proceedings of British Machine Vision Conference, pp. 1–12 (2012)
Danielsson, O.M., Carlsson, S., Sullivan, J.: Automatic learning and extraction of multi-local features. In: Proceedings of IEEE 12th International Conference on Computer Vision, pp. 917–924 (2009)
Fitzgibbon, A.W.: Robust registration of 2D and 3D point sets. In: British Machine Vision Conference, pp. 662–670 (2001)
Gold, S., Rangarajan, A., Lu, C.P., Mjolsness, E.: New algorithms for 2d and 3d point matching: pose estimation and correspondence. Pattern Recogn. 31, 957–964 (1997)
Jakubowicz, J., Morel, J.-M., Randall, G.: Lsd: a fast line segment detector with a false detection control. IEEE Trans. Pattern Anal. Mach. Intell. 32(4), 722–732 (2010)
Hinterstoisser, S., Cagniart, C., Ilic, S., Sturm, P.F., Navab, N., Fua, P., Lepetit, V.: Gradient response maps for real-time detection of textureless objects. IEEE Trans. Pattern Anal. Mach. Intell. 34(5), 876–888 (2012)
Jian, B., Vemuri, B.C.: Robust point set registration using gaussian mixture models. IEEE Trans. Pattern Anal. Mach. Intell. 33(8), 1633–1645 (2011)
Lian, W., Zhang, L.: Point matching in the presence of outliers in both point sets: a concave optimization approach. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 352–359 (2014)
Liu, M.-Y., Tuzel, O., Veeraraghavan, A., Taguchi, Y., Marks, T.K., Chellappa, R.: Fast object localization and pose estimation in heavy clutter for robotic bin picking. I. J. Robotic Res. 31(8), 951–973 (2012)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Ma, Y., Soatto, S., Kosecka, J., Sastry, S.: An invitation to 3D vision: from images to models. Springer, New York (2003)
Pretto, A., Tonello, S., Menegatti, E.: Flexible 3d localization of planar objects for industrial bin-picking with monocamera vision system. In: Proceedings of IEEE International Conference on Automation Science and Engineering (CASE), pp. 168–175 (2013)
Savarese, S., Li, F.-F.: 3d generic object categorization, localization and pose estimation. In: Proceedings of IEEE 11th International Conference on Computer Vision, pp. 1–8 (2007)
Shotton, J., Blake, A., Cipolla, R.: Multiscale categorical object recognition using contour fragments. IEEE Trans. Pattern Anal. Mach. Intell. 30(7), 1270–1281 (2008)
Sun, M., Kumar, S.S., Bradski, G.R., Savarese, S.: Object detection, shape recovery, and 3d modelling by depth-encoded hough voting. Comput. Vision Image Underst. 117(9), 1190–1202 (2013)
Tang, J., Miller, S., Singh, A., Abbeel, P.: A textured object recognition pipeline for color and depth image data. In: Proceedings of the IEEE International Conference on Robotics and Automation (2012)
Viksten, F., Forssén, P.-E., Johansson, B., Moe, A.: Comparison of local image descriptors for full 6 degree-of-freedom pose estimation. In: Proceedings of the IEEE International Conference on Robotics and Automation (2009)
Yeh, T., Lee, J.J., Darrell, T.: Fast concurrent object localization and recognition. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 280–287 (2009)
Acknowledgement
This research has been supported by the European Commission under: 644227-Flourish and FP7-ICT-601012 (RoCKIn Project).
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Imperoli, M., Pretto, A. (2015). D\(^{2}\)CO: Fast and Robust Registration of 3D Textureless Objects Using the Directional Chamfer Distance. In: Nalpantidis, L., Krüger, V., Eklundh, JO., Gasteratos, A. (eds) Computer Vision Systems. ICVS 2015. Lecture Notes in Computer Science(), vol 9163. Springer, Cham. https://doi.org/10.1007/978-3-319-20904-3_29
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