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[1] Yi Zhou,et al. On the Continuity of Rotation Representations in Neural Networks , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[2] Anoop Cherian,et al. Unsupervised Joint 3D Object Model Learning and 6D Pose Estimation for Depth-Based Instance Segmentation , 2019, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).
[3] Andrew Zisserman,et al. Learning To Count Objects in Images , 2010, NIPS.
[4] Shu Liu,et al. Path Aggregation Network for Instance Segmentation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[5] Rita Cucchiara,et al. SIFT-Based Segmentation of Multiple Instances of Low-Textured Objects , 2013 .
[6] Andrew Blake,et al. "GrabCut" , 2004, ACM Trans. Graph..
[7] Sebastian Ramos,et al. The Cityscapes Dataset for Semantic Urban Scene Understanding , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[8] R. Jonker,et al. Improving the Hungarian assignment algorithm , 1986 .
[9] Katsushi Ikeuchi,et al. Generating an interpretation tree from a CAD model for 3D-object recognition in bin-picking tasks , 1987, International Journal of Computer Vision.
[10] Andrea Vedaldi,et al. Instance Normalization: The Missing Ingredient for Fast Stylization , 2016, ArXiv.
[11] Kenneth Y. Goldberg,et al. Learning Deep Policies for Robot Bin Picking by Simulating Robust Grasping Sequences , 2017, CoRL.
[12] Christoph H. Lampert,et al. Unsupervised object-centric video generation and decomposition in 3D , 2020, NeurIPS.
[13] Leonidas J. Guibas,et al. GSPN: Generative Shape Proposal Network for 3D Instance Segmentation in Point Cloud , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[14] Ulrich Neumann,et al. SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[15] Timo Aila,et al. A Style-Based Generator Architecture for Generative Adversarial Networks , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[16] Gordon Wetzstein,et al. DeepVoxels: Learning Persistent 3D Feature Embeddings , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[17] Vladimir Kolmogorov,et al. An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision , 2001, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[18] Pietro Perona,et al. Microsoft COCO: Common Objects in Context , 2014, ECCV.
[19] Akira Nakamura,et al. Probabilistic approach for object bin picking approximated by cylinders , 2013, 2013 IEEE International Conference on Robotics and Automation.
[20] Daniel King,et al. Fetch & Freight : Standard Platforms for Service Robot Applications , 2016 .
[21] Dieter Fox,et al. Unseen Object Instance Segmentation for Robotic Environments , 2020, IEEE Transactions on Robotics.
[22] Li Fei-Fei,et al. CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[23] A. M. Hafiz,et al. A survey on instance segmentation: state of the art , 2020, International Journal of Multimedia Information Retrieval.
[24] Ronan Collobert,et al. Learning to Segment Object Candidates , 2015, NIPS.
[25] Michael I. Jordan,et al. On Spectral Clustering: Analysis and an algorithm , 2001, NIPS.
[26] Yongxiang Wu,et al. Deep instance segmentation and 6D object pose estimation in cluttered scenes for robotic autonomous grasping , 2020, Ind. Robot.
[27] Eric Brachmann,et al. iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects , 2017, ACCV.
[28] Manuel Graña,et al. Current Research Trends in Robot Grasping and Bin Picking , 2018, SOCO-CISIS-ICEUTE.
[29] Georg Heigold,et al. Object-Centric Learning with Slot Attention , 2020, NeurIPS.
[30] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[31] Dirk Buchholz. Bin-Picking: New Approaches for a Classical Problem , 2015 .
[32] Dieter Fox,et al. Learning RGB-D Feature Embeddings for Unseen Object Instance Segmentation , 2020, ArXiv.
[33] Simon Osindero,et al. Conditional Generative Adversarial Nets , 2014, ArXiv.
[34] 智一 吉田,et al. Efficient Graph-Based Image Segmentationを用いた圃場図自動作成手法の検討 , 2014 .
[35] Andrew Zisserman,et al. Spatial Transformer Networks , 2015, NIPS.
[36] Hongyuan Zha,et al. A Fast Proximal Point Method for Computing Exact Wasserstein Distance , 2018, UAI.
[37] Matthew Botvinick,et al. MONet: Unsupervised Scene Decomposition and Representation , 2019, ArXiv.
[38] Ross B. Girshick,et al. Fast R-CNN , 2015, 1504.08083.
[39] Levent Tunçel,et al. Optimization algorithms on matrix manifolds , 2009, Math. Comput..
[40] Klaus Greff,et al. Multi-Object Representation Learning with Iterative Variational Inference , 2019, ICML.
[41] Manolis I. A. Lourakis,et al. T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-Less Objects , 2017, 2017 IEEE Winter Conference on Applications of Computer Vision (WACV).
[42] Yiyi Liao,et al. Towards Unsupervised Learning of Generative Models for 3D Controllable Image Synthesis , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[43] Morgan Quigley,et al. ROS: an open-source Robot Operating System , 2009, ICRA 2009.
[44] Du Q. Huynh,et al. Metrics for 3D Rotations: Comparison and Analysis , 2009, Journal of Mathematical Imaging and Vision.
[45] Ross B. Girshick,et al. Mask R-CNN , 2017, 1703.06870.
[46] Tobias Ritschel,et al. Escaping Plato’s Cave: 3D Shape From Adversarial Rendering , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[47] Kaiming He,et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[48] Pascal Fua,et al. Segmentation-Driven 6D Object Pose Estimation , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[49] Lydia E. Kavraki,et al. Guided Expansive Spaces Trees: a search strategy for motion- and cost-constrained state spaces , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.
[50] Yong-Liang Yang,et al. HoloGAN: Unsupervised Learning of 3D Representations From Natural Images , 2019, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).