Abstract
This work addresses the problem of estimating the 6D Pose of specific objects from a single RGB-D image. We present a flexible approach that can deal with generic objects, both textured and texture-less. The key new concept is a learned, intermediate representation in form of a dense 3D object coordinate labelling paired with a dense class labelling. We are able to show that for a common dataset with texture-less objects, where template-based techniques are suitable and state of the art, our approach is slightly superior in terms of accuracy. We also demonstrate the benefits of our approach, compared to template-based techniques, in terms of robustness with respect to varying lighting conditions. Towards this end, we contribute a new ground truth dataset with 10k images of 20 objects captured each under three different lighting conditions. We demonstrate that our approach scales well with the number of objects and has capabilities to run fast.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Bo, L., Ren, X., Fox, D.: Unsupervised feature learning for RGB-D based object recognition. In: ISER (2012)
Criminisi, A., Shotton, J.: Decision Forests for Computer Vision and Medical Image Analysis. Springer (2013)
Damen, D., Bunnun, P., Calway, A., Mayol-Cuevas, W.: Real-time learning and detection of 3D texture-less objects: A scalable approach. In: BMVC (2012)
Drost, B., Ulrich, M., Navab, N., Ilic, S.: Model globally, match locally: Efficient and robust 3D object recognition. In: CVPR (2010)
Gall, J., Yao, A., Razavi, N., Van Gool, L., Lempitsky, V.: Hough Forests for object detection, tracking, and action recognition. IEEE Trans. on PAMI 33(11) (2011)
Girshick, R., Shotton, J., Kohli, P., Criminisi, A., Fitzgibbon, A.: Efficient regression of general-activity human poses from depth images. In: ICCV (2011)
Hinterstoisser, S., Cagniart, C., Ilic, S., Sturm, P., Navab, N., Fua, P., Lepetit, V.: Gradient response maps for real-time detection of texture-less objects. IEEE Trans. on PAMI (2012)
Hinterstoisser, S., Lepetit, V., Ilic, S., Holzer, S., Bradski, G., Konolige, K., Navab, N.: 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, Part I. LNCS, vol. 7724, pp. 548–562. Springer, Heidelberg (2013)
Hoiem, D., Rother, C., Winn, J.: 3D LayoutCRF for multi-view object class recognition and segmentation. In: CVPR (2007)
Holzer, S., Shotton, J., Kohli, P.: Learning to efficiently detect repeatable interest points in depth data. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part I. LNCS, vol. 7572, pp. 200–213. Springer, Heidelberg (2012)
Huttenlocher, D., Klanderman, G., Rucklidge, W.: Comparing images using the hausdorff distance. IEEE Trans. on PAMI (1993)
Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., Fitzgibbon, A.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: UIST (2011)
Lai, K., Bo, L., Ren, X., Fox, D.: A large-scale hierarchical multi-view rgb-d object dataset. In: ICRA. IEEE (2011)
Lepetit, V., Fua, P.: Keypoint recognition using randomized trees. IEEE Trans. on PAMI 28(9) (2006)
Lowe, D.G.: Local feature view clustering for 3d object recognition. In: CVPR (2001)
Martinez, M., Collet, A., Srinivasa, S.S.: Moped: A scalable and low latency object recognition and pose estimation system. In: ICRA (2010)
Newcombe, R., Izadi, S., Hilliges, O., Molyneaux, D., Kim, D., Davison, A., Kohli, P., Shotton, J., Hodges, S., Fitzgibbon, A.: KinectFusion: Real-time dense surface mapping and tracking. In: ISMAR (2011)
Nistér, D., Stewénius, H.: Scalable recognition with a vocabulary tree. In: CVPR (2006)
Ozuysal, M., Calonder, M., Lepetit, V., Fua, P.: Fast keypoint recognition using random ferns. IEEE Trans. on PAMI (2010)
Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Object retrieval with large vocabularies and fast spatial matching. In: CVPR (2007)
Rios-Cabrera, R., Tuytelaars, T.: Discriminatively trained templates for 3D object detection: A real time scalable approach. In: ICCV (2013)
Rosten, E., Porter, R., Drummond, T.: FASTER and better: A machine learning approach to corner detection. IEEE Trans. on PAMI 32 (2010)
Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., Blake, A.: Real-time human pose recognition in parts from a single depth image. In: CVPR (2011)
Shotton, J., Glocker, B., Zach, C., Izadi, S., Criminisi, A., Fitzgibbon, A.: Scene coordinate regression forests for camera relocalization in rgb-d images. In: CVPR (2013)
Shotton, J., Girshick, R.B., Fitzgibbon, A.W., Sharp, T., Cook, M., Finocchio, M., Moore, R., Kohli, P., Criminisi, A., Kipman, A., Blake, A.: Efficient human pose estimation from single depth images. IEEE Trans. on PAMI 35(12) (2013)
Steger, C.: Similarity measures for occlusion, clutter, and illumination invariant object recognition. In: DAGM-S (2001)
Sun, M., Bradski, G., Xu, B.-X., Savarese, S.: Depth-encoded hough voting for joint object detection and shape recovery. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 658–671. Springer, Heidelberg (2010)
Taylor, J., Shotton, J., Sharp, T., Fitzgibbon, A.: The Vitruvian Manifold: Inferring dense correspondences for one-shot human pose estimation. In: CVPR (2012)
Ferrari, V., Jurie, F., Schmid, C.: From images to shape models for object detection. In: IJCV (2009)
Winder, S., Hua, G., Brown, M.: Picking the best DAISY. In: CVPR (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
1 Electronic Supplementary Material
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Brachmann, E., Krull, A., Michel, F., Gumhold, S., Shotton, J., Rother, C. (2014). Learning 6D Object Pose Estimation Using 3D Object Coordinates. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds) Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8690. Springer, Cham. https://doi.org/10.1007/978-3-319-10605-2_35
Download citation
DOI: https://doi.org/10.1007/978-3-319-10605-2_35
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10604-5
Online ISBN: 978-3-319-10605-2
eBook Packages: Computer ScienceComputer Science (R0)