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IEEE , 2011 . Hinterstoisser, Stefan, et al. \"Multimodal templates for real-time detection of texture-less objects in heavily cluttered scenes.\" 2011 international conference on computer vision. IEEE, 2011."},{"key":"e_1_3_2_1_2_1","volume-title":"Posecnn: A convolutional neural network for 6d object pose estimation in cluttered scenes.\" arXiv preprint arXiv:1711.00199","author":"Xiang Yu","year":"2017","unstructured":"Xiang , Yu , et al. \" Posecnn: A convolutional neural network for 6d object pose estimation in cluttered scenes.\" arXiv preprint arXiv:1711.00199 ( 2017 ). Xiang, Yu, et al. \"Posecnn: A convolutional neural network for 6d object pose estimation in cluttered scenes.\" arXiv preprint arXiv:1711.00199 (2017)."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"crossref","unstructured":"Bay Herbert et al. \"Speeded-up robust features (SURF).\" Computer vision and image understanding 110.3 (2008):  346--359.  Bay Herbert et al. \"Speeded-up robust features (SURF).\" Computer vision and image understanding 110.3 (2008): 346--359.","DOI":"10.1016\/j.cviu.2007.09.014"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"crossref","unstructured":"Hinterstoisser Stefan et al. \"Gradient response maps for real-time detection of textureless objects.\" IEEE transactions on pattern analysis and machine intelligence 34.5 (2011):  876--888.  Hinterstoisser Stefan et al. \"Gradient response maps for real-time detection of textureless objects.\" IEEE transactions on pattern analysis and machine intelligence 34.5 (2011): 876--888.","DOI":"10.1109\/TPAMI.2011.206"},{"key":"e_1_3_2_1_5_1","volume-title":"Ieee","author":"Lowe David G","year":"1999","unstructured":"Lowe , David G . \" Object recognition from local scale-invariant features.\" Proceedings of the seventh IEEE international conference on computer vision. Vol. 2 . Ieee , 1999 . Lowe, David G. \"Object recognition from local scale-invariant features.\" Proceedings of the seventh IEEE international conference on computer vision. Vol. 2. Ieee, 1999."},{"key":"e_1_3_2_1_6_1","volume-title":"Ssd-6d: Making rgb-based 3d detection and 6d pose estimation great again.\" Proceedings of the IEEE International Conference on Computer Vision","author":"Kehl Wadim","year":"2017","unstructured":"Kehl , Wadim , et al. \" Ssd-6d: Making rgb-based 3d detection and 6d pose estimation great again.\" Proceedings of the IEEE International Conference on Computer Vision . 2017 . Kehl, Wadim, et al. \"Ssd-6d: Making rgb-based 3d detection and 6d pose estimation great again.\" Proceedings of the IEEE International Conference on Computer Vision. 2017."},{"key":"e_1_3_2_1_7_1","volume-title":"Learning 6d object pose estimation using 3d object coordinates.\" European conference on computer vision","author":"Brachmann Eric","year":"2014","unstructured":"Brachmann , Eric , et al. \" Learning 6d object pose estimation using 3d object coordinates.\" European conference on computer vision . Springer , Cham , 2014 . Brachmann, Eric, et al. \"Learning 6d object pose estimation using 3d object coordinates.\" European conference on computer vision. Springer, Cham, 2014."},{"key":"e_1_3_2_1_8_1","volume-title":"A scalable, accurate, robust to partial occlusion method for predicting the 3D poses of challenging objects without using depth.\" Proceedings of the IEEE International Conference on Computer Vision","author":"Rad Mahdi","year":"2017","unstructured":"Rad , Mahdi , and Vincent Lepetit . \"BB8 : A scalable, accurate, robust to partial occlusion method for predicting the 3D poses of challenging objects without using depth.\" Proceedings of the IEEE International Conference on Computer Vision . 2017 . Rad, Mahdi, and Vincent Lepetit. \"BB8: A scalable, accurate, robust to partial occlusion method for predicting the 3D poses of challenging objects without using depth.\" Proceedings of the IEEE International Conference on Computer Vision. 2017."},{"key":"e_1_3_2_1_9_1","volume-title":"IEEE","author":"Ren Xiaofeng","year":"2012","unstructured":"Ren , Xiaofeng , Liefeng Bo , and Dieter Fox . \"Rgb-(d) scene labeling : Features and algorithms.\" 2012 IEEE Conference on Computer Vision and Pattern Recognition . IEEE , 2012 . Ren, Xiaofeng, Liefeng Bo, and Dieter Fox. \"Rgb-(d) scene labeling: Features and algorithms.\" 2012 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2012."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","unstructured":"Rothganger Fred et al. \"3d object modeling and recognition using local affine-invariant image descriptors and multi-view spatial constraints.\" International journal of computer vision 66.3 (2006):  231--259.  Rothganger Fred et al. \"3d object modeling and recognition using local affine-invariant image descriptors and multi-view spatial constraints.\" International journal of computer vision 66.3 (2006): 231--259.","DOI":"10.1007\/s11263-005-3674-1"},{"key":"e_1_3_2_1_11_1","volume-title":"Glampoints: Greedily learned accurate match points.\" Proceedings of the IEEE International Conference on Computer Vision","author":"Truong Prune","year":"2019","unstructured":"Truong , Prune , et al. \" Glampoints: Greedily learned accurate match points.\" Proceedings of the IEEE International Conference on Computer Vision . 2019 . Truong, Prune, et al. \"Glampoints: Greedily learned accurate match points.\" Proceedings of the IEEE International Conference on Computer Vision. 2019."},{"key":"e_1_3_2_1_12_1","volume-title":"Learning rich features from RGB-D images for object detection and segmentation.\" European conference on computer vision","author":"Gupta Saurabh","year":"2014","unstructured":"Gupta , Saurabh , et al. \" Learning rich features from RGB-D images for object detection and segmentation.\" European conference on computer vision . Springer , Cham , 2014 . Gupta, Saurabh, et al. \"Learning rich features from RGB-D images for object detection and segmentation.\" European conference on computer vision. Springer, Cham, 2014."},{"key":"e_1_3_2_1_13_1","volume-title":"Grasp2vec: Learning object representations from self-supervised grasping.\" arXiv preprint arXiv:1811.06964","author":"Jang Eric","year":"2018","unstructured":"Jang , Eric , et al. \" Grasp2vec: Learning object representations from self-supervised grasping.\" arXiv preprint arXiv:1811.06964 ( 2018 ). Jang, Eric, et al. \"Grasp2vec: Learning object representations from self-supervised grasping.\" arXiv preprint arXiv:1811.06964 (2018)."},{"key":"e_1_3_2_1_14_1","volume-title":"Learning dense visual object descriptors by and for robotic manipulation.\" arXiv preprint arXiv:1806.08756","author":"Florence Peter R","year":"2018","unstructured":"Florence , Peter R . , Lucas Manuelli , and Russ Tedrake . \"Dense object nets : Learning dense visual object descriptors by and for robotic manipulation.\" arXiv preprint arXiv:1806.08756 ( 2018 ). Florence, Peter R., Lucas Manuelli, and Russ Tedrake. \"Dense object nets: Learning dense visual object descriptors by and for robotic manipulation.\" arXiv preprint arXiv:1806.08756 (2018)."},{"key":"e_1_3_2_1_15_1","volume-title":"Springer","author":"Bo Liefeng","year":"2013","unstructured":"Bo , Liefeng , Xiaofeng Ren , and Dieter Fox . \" Unsupervised feature learning for RGB-D based object recognition.\" Experimental robotics . Springer , Heidelberg , 2013 . Bo, Liefeng, Xiaofeng Ren, and Dieter Fox. \"Unsupervised feature learning for RGB-D based object recognition.\" Experimental robotics. Springer, Heidelberg, 2013."},{"key":"e_1_3_2_1_16_1","volume-title":"Ieee","author":"Rublee Ethan","year":"2011","unstructured":"Rublee , Ethan , et al. \" ORB : An efficient alternative to SIFT or SURF.\" 2011 International conference on computer vision . Ieee , 2011 . Rublee, Ethan, et al. \"ORB: An efficient alternative to SIFT or SURF.\" 2011 International conference on computer vision. Ieee, 2011."},{"key":"e_1_3_2_1_17_1","volume-title":"Densefusion: 6d object pose estimation by iterative dense fusion.\" Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Wang Chen","year":"2019","unstructured":"Wang , Chen , et al. \" Densefusion: 6d object pose estimation by iterative dense fusion.\" Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 2019 . Wang, Chen, et al. \"Densefusion: 6d object pose estimation by iterative dense fusion.\" Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019."},{"key":"e_1_3_2_1_18_1","volume-title":"McKay. \"Method for registration of 3-D shapes.\" Sensor fusion IV: control paradigms and data structures","author":"Besl Paul J","year":"1992","unstructured":"Besl , Paul J . , and Neil D . McKay. \"Method for registration of 3-D shapes.\" Sensor fusion IV: control paradigms and data structures . Vol. 1611 . International Society for Optics and Photonics , 1992 . Besl, Paul J., and Neil D. McKay. \"Method for registration of 3-D shapes.\" Sensor fusion IV: control paradigms and data structures. Vol. 1611. International Society for Optics and Photonics, 1992."},{"key":"e_1_3_2_1_19_1","volume-title":"Model based training, detection and pose estimation of texture-less 3d objects in heavily cluttered scenes.\" Asian conference on computer vision","author":"Hinterstoisser Stefan","year":"2012","unstructured":"Hinterstoisser , Stefan , et al. \" Model based training, detection and pose estimation of texture-less 3d objects in heavily cluttered scenes.\" Asian conference on computer vision . Springer , Berlin, Heidelberg , 2012 . Hinterstoisser, Stefan, et al. \"Model based training, detection and pose estimation of texture-less 3d objects in heavily cluttered scenes.\" Asian conference on computer vision. Springer, Berlin, Heidelberg, 2012."},{"key":"e_1_3_2_1_20_1","unstructured":"Liu Jin and Sheng He. \"6D Object Pose Estimation without PnP.\" arXiv preprint arXiv:1902.01728 (2019).  Liu Jin and Sheng He. \"6D Object Pose Estimation without PnP.\" arXiv preprint arXiv:1902.01728 (2019)."},{"key":"e_1_3_2_1_21_1","unstructured":"Choy Christopher B. et al. \"Universal correspondence network.\" Advances in Neural Information Processing Systems. 2016.  Choy Christopher B. et al. \"Universal correspondence network.\" Advances in Neural Information Processing Systems. 2016."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2016.2634089"},{"key":"e_1_3_2_1_23_1","volume-title":"Scene coordinate regression forests for camera relocalization in RGB-D images.\" Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Shotton Jamie","year":"2013","unstructured":"Shotton , Jamie , et al. \" Scene coordinate regression forests for camera relocalization in RGB-D images.\" Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 2013 . Shotton, Jamie, et al. \"Scene coordinate regression forests for camera relocalization in RGB-D images.\" Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2013."},{"key":"e_1_3_2_1_24_1","volume-title":"3dmatch: Learning local geometric descriptors from RGB-D reconstructions.\" Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Zeng Andy","year":"2017","unstructured":"Zeng , Andy , et al. \" 3dmatch: Learning local geometric descriptors from RGB-D reconstructions.\" Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 2017 . Zeng, Andy, et al. \"3dmatch: Learning local geometric descriptors from RGB-D reconstructions.\" Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017."},{"key":"e_1_3_2_1_25_1","volume-title":"McKay. \"Method for registration of 3-D shapes.\" Sensor fusion IV: control paradigms and data structures","author":"Besl Paul J","year":"1992","unstructured":"Besl , Paul J . , and Neil D . McKay. \"Method for registration of 3-D shapes.\" Sensor fusion IV: control paradigms and data structures . Vol. 1611 . International Society for Optics and Photonics , 1992 . Besl, Paul J., and Neil D. McKay. \"Method for registration of 3-D shapes.\" Sensor fusion IV: control paradigms and data structures. Vol. 1611. International Society for Optics and Photonics, 1992."},{"key":"e_1_3_2_1_26_1","volume-title":"Deep learning on point sets for 3d classification and segmentation.\" Proceedings of the IEEE conference on computer vision and pattern recognition","author":"Qi Charles R","year":"2017","unstructured":"Qi , Charles R . , et al. \" Pointnet : Deep learning on point sets for 3d classification and segmentation.\" Proceedings of the IEEE conference on computer vision and pattern recognition . 2017 . Qi, Charles R., et al. \"Pointnet: Deep learning on point sets for 3d classification and segmentation.\" Proceedings of the IEEE conference on computer vision and pattern recognition. 2017."},{"key":"e_1_3_2_1_27_1","volume-title":"Deep sensor fusion for 3d bounding box estimation.\" Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Xu Danfei","year":"2018","unstructured":"Xu , Danfei , Dragomir Anguelov , and Ashesh Jain . \" Pointfusion : Deep sensor fusion for 3d bounding box estimation.\" Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 2018 . Xu, Danfei, Dragomir Anguelov, and Ashesh Jain. \"Pointfusion: Deep sensor fusion for 3d bounding box estimation.\" Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018."},{"key":"e_1_3_2_1_28_1","volume-title":"Deep learning of local RGB-D patches for 3d object detection and 6d pose estimation.\" European conference on computer vision","author":"Kehl Wadim","year":"2016","unstructured":"Kehl , Wadim , et al. \" Deep learning of local RGB-D patches for 3d object detection and 6d pose estimation.\" European conference on computer vision . Springer , Cham , 2016 . Kehl, Wadim, et al. \"Deep learning of local RGB-D patches for 3d object detection and 6d pose estimation.\" European conference on computer vision. 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