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The ability to segment unknown objects in depth images has potential to enhance robot skills in grasping and object tracking.
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B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik, “Hypercolumns for object segmentation and fine-grained localization,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) , 2015, pp. 447–456
2015
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A.-V. Vo, L. Truong-Hong, D. F. Laefer, and M. Bertolotto, “Octree-based region growing for point cloud segmentation,” ISPRS Journal of Photogrammetry and Remote Sensing , vol. 104, pp. 88–100, 2015
2015
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Y.-T. Chen, X. Liu, and M.-H. Yang, “Multi-instance object segmentation with occlusion handling,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) , 2015, pp. 3470–3478
2015
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H. Su, C. R. Qi, Y. Li, and L. J. Guibas, “Render for cnn: Viewpoint estimation in images using cnns trained with rendered 3d model views,” in Proc. IEEE Int. Conf. on Computer Vision (ICCV) , 2015, pp. 2686–2694
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R. Jonschkowski, C. Eppner, S. Höfer, R. Martín-Martín, and O. Brock, “Probabilistic multi-class segmentation for the amazon picking challenge,” in Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) . IEEE, 2016, pp. 1–7
2016
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G. Ros, L. Sellart, J. Materzynska, D. Vazquez, and A. M. Lopez, “The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) , 2016, pp. 3234–3243
2016
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P. O. Pinheiro, T.-Y. Lin, R. Collobert, and P. Dollár, “Learning to refine object segments,” in European Conference on Computer Vision . Springer, 2016, pp. 75–91
2016
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C. Eppner, S. Höfer, R. Jonschkowski, R. M. Martin, A. Sieverling, V. Wall, and O. Brock, “Lessons from the amazon picking challenge: Four aspects of building robotic systems.” in Robotics: Science and Systems , 2016
2016
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2017
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J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, and P. Abbeel, “Domain randomization for transferring deep neural networks from simulation to the real world,” in Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) . IEEE, 2017, pp. 23–30
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2017
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2017
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L. Ye, Z. Liu, and Y. Wang, “Depth-aware object instance segmentation,” in Image Processing (ICIP), 2017 IEEE International Conference on . IEEE, 2017, pp. 325–329
2017
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2017
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J. Mahler and K. Goldberg, “Learning deep policies for robot bin picking by simulating robust grasping sequences,” in Conf. on Robot Learning (CoRL) , 2017, pp. 515–524
2017
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A. ten Pas and R. Platt, “Using geometry to detect grasp poses in 3d point clouds,” in Robotics Research . Springer, 2018, pp. 307–324
2018
Closest in time.
2018
Closest in time.
X. Chen, K. Kundu, Y. Zhu, H. Ma, S. Fidler, and R. Urtasun, “3d object proposals using stereo imagery for accurate object class detection,” IEEE transactions on pattern analysis and machine intelligence , vol. 40, no. 5, pp. 1259–1272, 2018
2018
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W. Wang, R. Yu, Q. Huang, and U. Neumann, “Sgpn: Similarity group proposal network for 3d point cloud instance segmentation,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) , 2018
2018
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P. Marion, P. R. Florence, L. Manuelli, and R. Tedrake, “Labelfusion: A pipeline for generating ground truth labels for real rgbd data of cluttered scenes,” in Proc. IEEE Int. Conf. Robotics and Automation (ICRA) , 2018
2018
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W. Abdulla, “Mask r-cnn for object detection and instance segmentation on keras and tensorflow,” https://github.com/matterport/Mask_RCNN/
2018
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