A Summary of the 4th International Workshop on Recovering 6D Object Pose

This document summarizes the 4th International Workshop on Recovering 6D Object Pose which was organized in conjunction with ECCV 2018 in Munich. The workshop featured four invited talks, oral and poster presentations of accepted workshop papers, and an introduction of the BOP benchmark for 6D object pose estimation. The workshop was attended by 100+ people working on relevant topics in both academia and industry who shared up-to-date advances and discussed open problems.

[1]  Mathieu Aubry,et al.  3D-CODED: 3D Correspondences by Deep Deformation , 2018, ECCV.

[2]  Kostas E. Bekris,et al.  Improving 6D Pose Estimation of Objects in Clutter Via Physics-Aware Monte Carlo Tree Search , 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[3]  Federico Tombari,et al.  Unique Signatures of Histograms for Local Surface Description , 2010, ECCV.

[4]  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.

[5]  Kostas E. Bekris,et al.  Robust 6D Object Pose Estimation with Stochastic Congruent Sets , 2018, BMVC.

[6]  Huimin Ma,et al.  3D Object Proposals for Accurate Object Class Detection , 2015, NIPS.

[7]  Vincent Lepetit,et al.  BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depth , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[8]  Sanja Fidler,et al.  Monocular 3D Object Detection for Autonomous Driving , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Vladlen Koltun,et al.  Learning Compact Geometric Features , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[10]  Nassir Navab,et al.  Deep Learning of Local RGB-D Patches for 3D Object Detection and 6D Pose Estimation , 2016, ECCV.

[11]  Nassir Navab,et al.  Fully-Convolutional Point Networks for Large-Scale Point Clouds , 2018, ECCV.

[12]  Eric Brachmann,et al.  BOP: Benchmark for 6D Object Pose Estimation , 2018, ECCV.

[13]  Matthias Nießner,et al.  3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Federico Tombari,et al.  CNN-SLAM: Real-Time Dense Monocular SLAM with Learned Depth Prediction , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Nassir Navab,et al.  Model globally, match locally: Efficient and robust 3D object recognition , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[16]  Chyi-Yeu Lin,et al.  6D pose estimation using an improved method based on point pair features , 2018, 2018 4th International Conference on Control, Automation and Robotics (ICCAR).

[17]  Dirk Kraft,et al.  Rotational Subgroup Voting and Pose Clustering for Robust 3D Object Recognition , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[18]  Nassir Navab,et al.  Deep Model-Based 6D Pose Refinement in RGB , 2018, ECCV.

[19]  Oliver Brock,et al.  Analysis and Observations From the First Amazon Picking Challenge , 2016, IEEE Transactions on Automation Science and Engineering.

[20]  Mathieu Aubry,et al.  AtlasNet: A Papier-M\^ach\'e Approach to Learning 3D Surface Generation , 2018, CVPR 2018.

[21]  Mathieu Aubry,et al.  A Papier-Mache Approach to Learning 3D Surface Generation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[22]  Martijn Wisse,et al.  Team Delft's Robot Winner of the Amazon Picking Challenge 2016 , 2016, RoboCup.

[23]  Kostas E. Bekris,et al.  A self-supervised learning system for object detection using physics simulation and multi-view pose estimation , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[24]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[25]  Pascal Fua,et al.  Real-Time Seamless Single Shot 6D Object Pose Prediction , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[26]  Norbert Krüger,et al.  Local shape feature fusion for improved matching, pose estimation and 3D object recognition , 2016, SpringerPlus.

[27]  Stepán Obdrzálek,et al.  On Evaluation of 6D Object Pose Estimation , 2016, ECCV Workshops.

[28]  Eric Brachmann,et al.  Uncertainty-Driven 6D Pose Estimation of Objects and Scenes from a Single RGB Image , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[29]  Tae-Kyun Kim,et al.  Latent-Class Hough Forests for 3D Object Detection and Pose Estimation , 2014, ECCV.

[30]  Yue Wang,et al.  Dynamic Graph CNN for Learning on Point Clouds , 2018, ACM Trans. Graph..

[31]  Nico Blodow,et al.  Fast Point Feature Histograms (FPFH) for 3D registration , 2009, 2009 IEEE International Conference on Robotics and Automation.

[32]  Michael J. Black,et al.  FAUST: Dataset and Evaluation for 3D Mesh Registration , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[33]  Nassir Navab,et al.  SSD-6D: Making RGB-Based 3D Detection and 6D Pose Estimation Great Again , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[34]  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).

[35]  Markus Ulrich,et al.  Introducing MVTec ITODD — A Dataset for 3D Object Recognition in Industry , 2017, 2017 IEEE International Conference on Computer Vision Workshops (ICCVW).

[36]  Tae-Kyun Kim,et al.  Recovering 6D Object Pose and Predicting Next-Best-View in the Crowd , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[37]  Manolis I. A. Lourakis,et al.  Detection and fine 3D pose estimation of texture-less objects in RGB-D images , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[38]  Ian D. Reid,et al.  Deep-6DPose: Recovering 6D Object Pose from a Single RGB Image , 2018, ArXiv.

[39]  Vincent Lepetit,et al.  Model Based Training, Detection and Pose Estimation of Texture-Less 3D Objects in Heavily Cluttered Scenes , 2012, ACCV.

[40]  Leonidas J. Guibas,et al.  PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[41]  Eric Brachmann,et al.  Learning 6D Object Pose Estimation Using 3D Object Coordinates , 2014, ECCV.

[42]  N. Mitra,et al.  Fast Global Pointcloud Registration via Smart Indexing , 2014 .

[43]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[44]  Kuan-Ting Yu,et al.  Multi-view self-supervised deep learning for 6D pose estimation in the Amazon Picking Challenge , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[45]  Kostas E. Bekris,et al.  A Dataset for Improved RGBD-Based Object Detection and Pose Estimation for Warehouse Pick-and-Place , 2015, IEEE Robotics and Automation Letters.

[46]  Nassir Navab,et al.  Looking Beyond the Simple Scenarios: Combining Learners and Optimizers in 3D Temporal Tracking , 2017, IEEE Transactions on Visualization and Computer Graphics.

[47]  Paul J. Besl,et al.  Method for registration of 3-D shapes , 1992, Other Conferences.

[48]  Dieter Fox,et al.  PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes , 2017, Robotics: Science and Systems.