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non-rigid ICP [2] as an option to further improve the pose estimation. • The preparation of an extensive annotated dataset will lead to an objective evaluation of our approach with various parameters and settings and a comparison to state of the art methods. • Here, we assumed a correct segmentation result. In future we need to investigate optimal segmentation methods for real world experiments. ACKNOWLEDGMENT The research leading to these results has received funding from the European Community’s Seventh Framework Pro- gramme FP7/2007-2013 under grant agreement No. 600623, STRANDS and industrial funding from OMRON Corpora- tion in Japan REFERENCES [1] A. Aldoma, F. Tombari, L. Di Stefano, and M. Vincze, “A global hypothesis verification framework for 3d object recognition in clutter,” IEEE transactions on pattern analysis and machine intelligence, vol. 38, no. 7, pp. 1383–1396, 2016. [2] B. Amberg, S. Romdhani, and T. Vetter, “Optimal step nonrigid icp algorithms for surface registration,” in Computer Vision and Pattern Recognition, 2007. CVPR’07. IEEE Conference on. IEEE, 2007, pp. 1–8. [3] F. M. Carlucci, P. Russo, and B. Caputo, “A deep representation for depth images from synthetic data,” arXiv preprint arXiv:1609.09713, 2016. [4] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on. IEEE, 2009, pp. 248–255. [5] A. Doumanoglou, V. Balntas, R. Kouskouridas, and T.-K. Kim, “Siamese regression networks with efficient mid-level feature extrac- tion for 3d object pose estimation,” arXiv preprint arXiv:1607.02257, 2016. [6] A. Eitel, J. T. Springenberg, L. Spinello, M. Riedmiller, and W. Bur- gard, “Multimodal deep learning for robust rgb-d object recognition,” in Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ Interna- tional Conference on. IEEE, 2015, pp. 681–687. [7] M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisser- man, “The pascal visual object classes (voc) challenge,” International journal of computer vision, vol. 88, no. 2, pp. 303–338, 2010. [8] R. Hadsell, S. Chopra, and Y. LeCun, “Dimensionality reduction by learning an invariant mapping,” in Computer vision and pattern recognition,2006IEEEcomputer societyconferenceon, vol. 2. IEEE, 2006, pp. 1735–1742. [9] S.Hinterstoisser,V.Lepetit,S. Ilic,S.Holzer,G.Bradski,K.Konolige, and N. Navab, “Model based training, detection and pose estimation of texture-less3dobjects inheavilyclutteredscenes,” inAsianconference on computer vision. Springer, 2012, pp. 548–562. [10] T. Hodan, P. Haluza, S. Obdrzalek, J. Matas, M. Lourakis, and X. Zabulis, “T-less: An rgb-d dataset for 6d pose estimation of texture- less objects,” arXiv preprint arXiv:1701.05498, 2017. [11] Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Convolutional architecture for fast featureembedding,” inProceedingsof the22ndACMinternational conference on Multimedia. ACM, 2014, pp. 675–678. [12] A. Kendall, M. Grimes, and R. Cipolla, “Posenet: A convolutional network for real-time 6-dof camera relocalization,” in Proceedings of the IEEE international conference on computer vision, 2015, pp. 2938–2946. [13] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems, 2012, pp. 1097–1105. [14] E. Mun˜oz, Y. Konishi, V. Murino, and A. Del Bue, “Fast 6d pose estimation for texture-less objects from a single rgb image,” in RoboticsandAutomation(ICRA),2016IEEEInternationalConference on. IEEE, 2016, pp. 5623–5630. [15] S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real- time object detection with region proposal networks,” in Advances in neural information processing systems, 2015, pp. 91–99. [16] M. Schwarz, H. Schulz, and S. Behnke, “Rgb-d object recognition and poseestimationbasedonpre-trainedconvolutional neural network fea- tures,” in Robotics and Automation (ICRA), 2015 IEEE International Conference on. IEEE, 2015, pp. 1329–1335. [17] F. Tombari, S. Salti, and L. Di Stefano, “Unique signatures of histograms for local surface description,” in European Conference on Computer Vision. Springer, 2010, pp. 356–369. [18] P. Wohlhart and V. Lepetit, “Learning descriptors for object recogni- tion and 3d pose estimation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 3109–3118. [19] W. Wohlkinger and M. Vincze, “Ensemble of shape functions for 3d object classification,” in 2011 IEEE International Conference on Robotics and Biomimetics, Dec 2011, pp. 2987–2992. 91