HFF6D: Hierarchical Feature Fusion Network for Robust 6D Object Pose Tracking
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Jian Liu | Wei Sun | Xing Zhang | Wei Wu | Shimeng Fan | Chongpei Liu
[1] Qifeng Yu,et al. Robust Monocular Pose Tracking of Less-Distinct Objects Based on Contour-Part Model , 2021, IEEE Transactions on Circuits and Systems for Video Technology.
[2] Kostas E. Bekris,et al. BundleTrack: 6D Pose Tracking for Novel Objects without Instance or Category-Level 3D Models , 2021, 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[3] Giuseppe Loianno,et al. VIPose: Real-time Visual-Inertial 6D Object Pose Tracking , 2021, 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[4] Henglin Shi,et al. iMiGUE: An Identity-free Video Dataset for Micro-Gesture Understanding and Emotion Analysis , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[5] Kostas E. Bekris,et al. Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domains , 2021, ArXiv.
[6] Jianwei Guo,et al. Efficient Center Voting for Object Detection and 6D Pose Estimation in 3D Point Cloud , 2021, IEEE Transactions on Image Processing.
[7] Jiguang Yue,et al. Accurate 6DOF Pose Tracking for Texture-Less Objects , 2021, IEEE Transactions on Circuits and Systems for Video Technology.
[8] Rio Yokota,et al. RePOSE: Fast 6D Object Pose Refinement via Deep Texture Rendering , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[9] Haoqiang Fan,et al. FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[10] A. Yuille,et al. NeMo: Neural Mesh Models of Contrastive Features for Robust 3D Pose Estimation , 2021, ICLR.
[11] P. Maragos,et al. How to track your dragon: A Multi-Attentional Framework for real-time RGB-D 6-DOF Object Pose Tracking , 2020, ECCV Workshops.
[12] Josef Kittler,et al. Complementary Discriminative Correlation Filters Based on Collaborative Representation for Visual Object Tracking , 2020, IEEE Transactions on Circuits and Systems for Video Technology.
[13] Sven Behnke,et al. Refining 6D Object Pose Predictions using Abstract Render-and-Compare , 2019, 2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids).
[14] Timothy Bretl,et al. PoseRBPF: A Rao–Blackwellized Particle Filter for 6-D Object Pose Tracking , 2019, IEEE Transactions on Robotics.
[15] Silvio Savarese,et al. DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[16] Hujun Bao,et al. PVNet: Pixel-Wise Voting Network for 6DoF Pose Estimation , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[17] Andreas Wieser,et al. The Perfect Match: 3D Point Cloud Matching With Smoothed Densities , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[18] Dieter Fox,et al. Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objects , 2018, CoRL.
[19] Ming Lu,et al. A Direct 3D Object Tracking Method Based on Dynamic Textured Model Rendering and Extended Dense Feature Fields , 2018, IEEE Transactions on Circuits and Systems for Video Technology.
[20] Stanley T. Birchfield,et al. Falling Things: A Synthetic Dataset for 3D Object Detection and Pose Estimation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[21] Vincent Lepetit,et al. Making Deep Heatmaps Robust to Partial Occlusions for 3D Object Pose Estimation , 2018, ECCV.
[22] Yi Li,et al. DeepIM: Deep Iterative Matching for 6D Pose Estimation , 2018, International Journal of Computer Vision.
[23] Gregory D. Hager,et al. A Unified Framework for Multi-View Multi-Class Object Pose Estimation , 2018, ECCV.
[24] Ian D. Reid,et al. Deep-6DPose: Recovering 6D Object Pose from a Single RGB Image , 2018, ArXiv.
[25] Vincent Lepetit,et al. Feature Mapping for Learning Fast and Accurate 3D Pose Inference from Synthetic Images , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[26] Dieter Fox,et al. PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes , 2017, Robotics: Science and Systems.
[27] Ulrich Schwanecke,et al. Real-Time Monocular Pose Estimation of 3D Objects Using Temporally Consistent Local Color Histograms , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[28] 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).
[29] 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).
[30] P. Abbeel,et al. Domain randomization for transferring deep neural networks from simulation to the real world , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[31] 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).
[32] 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).
[33] Eduardo Ros,et al. Real-Time Pose Detection and Tracking of Hundreds of Objects , 2016, IEEE Transactions on Circuits and Systems for Video Technology.
[34] Ulrich Schwanecke,et al. Real-Time Monocular Segmentation and Pose Tracking of Multiple Objects , 2016, ECCV.
[35] Matthias Nießner,et al. 3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[36] Stefan Schaal,et al. Depth-based object tracking using a Robust Gaussian Filter , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).
[37] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[38] Dieter Fox,et al. DART: Dense Articulated Real-Time Tracking , 2014, Robotics: Science and Systems.
[39] Stefan Schaal,et al. Probabilistic object tracking using a range camera , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.
[40] Henrik I. Christensen,et al. 3D textureless object detection and tracking: An edge-based approach , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.
[41] Vincent Lepetit,et al. Model Based Training, Detection and Pose Estimation of Texture-Less 3D Objects in Heavily Cluttered Scenes , 2012, ACCV.
[42] Ian D. Reid,et al. PWP3D: Real-Time Segmentation and Tracking of 3D Objects , 2012, International Journal of Computer Vision.
[43] Gary R. Bradski,et al. ORB: An efficient alternative to SIFT or SURF , 2011, 2011 International Conference on Computer Vision.
[44] 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.
[45] Henrik I. Christensen,et al. Real-time 3D model-based tracking using edge and keypoint features for robotic manipulation , 2010, 2010 IEEE International Conference on Robotics and Automation.
[46] T. Tuytelaars,et al. SURF: Speeded Up Robust Features , 2006, ECCV.
[47] Vincent Lepetit,et al. Combining edge and texture information for real-time accurate 3D camera tracking , 2004, Third IEEE and ACM International Symposium on Mixed and Augmented Reality.
[48] David G. Lowe,et al. Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.
[49] Robert C. Bolles,et al. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.
[50] W. Kabsch. A solution for the best rotation to relate two sets of vectors , 1976 .
[51] Chen Qijun,et al. A Novel Depth and Color Feature Fusion Framework for 6D Object Pose Estimation , 2021, IEEE Transactions on Multimedia.
[52] Chris Harris,et al. RAPID - a video rate object tracker , 1990, BMVC.