Wide-Depth-Range 6D Object Pose Estimation in Space

6D pose estimation in space poses unique challenges that are not commonly encountered in the terrestrial setting. One of the most striking differences is the lack of atmospheric scattering, allowing objects to be visible from a great distance while complicating illumination conditions. Currently available benchmark datasets do not place a sufficient emphasis on this aspect and mostly depict the target in close proximity.Prior work tackling pose estimation under large scale variations relies on a two-stage approach to first estimate scale, followed by pose estimation on a resized image patch. We instead propose a single-stage hierarchical end-to-end trainable network that is more robust to scale variations. We demonstrate that it outperforms existing approaches not only on images synthesized to resemble images taken in space but also on standard benchmarks.

[1]  Yunsong Li,et al.  Highly accurate optical flow estimation on superpixel tree , 2016, Image Vis. Comput..

[2]  Jitendra Malik,et al.  Viewpoints and keypoints , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

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

[5]  Vincent Lepetit,et al.  DAISY: An Efficient Dense Descriptor Applied to Wide-Baseline Stereo , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  D. Izzo,et al.  Satellite Pose Estimation Challenge: Dataset, Competition Design, and Results , 2019, IEEE Transactions on Aerospace and Electronic Systems.

[7]  Éric Marchand,et al.  Vision-based space autonomous rendezvous: A case study , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[8]  Vincent Lepetit,et al.  Learning Image Descriptors with the Boosting-Trick , 2012, NIPS.

[9]  Xiangyang Ji,et al.  CDPN: Coordinates-Based Disentangled Pose Network for Real-Time RGB-Based 6-DoF Object Pose Estimation , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[10]  Gregory D. Hager,et al.  Fast and Globally Convergent Pose Estimation from Video Images , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  V. Lepetit,et al.  EPnP: An Accurate O(n) Solution to the PnP Problem , 2009, International Journal of Computer Vision.

[12]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[13]  Simone D'Amico,et al.  Robust Model-Based Monocular Pose Initialization for Noncooperative Spacecraft Rendezvous , 2018, Journal of Spacecraft and Rockets.

[14]  Kaiming He,et al.  Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Vincent Lepetit,et al.  Making Deep Heatmaps Robust to Partial Occlusions for 3D Object Pose Estimation , 2018, ECCV.

[16]  Ali Farhadi,et al.  YOLOv3: An Incremental Improvement , 2018, ArXiv.

[17]  Jiaru Song,et al.  HybridPose: 6D Object Pose Estimation Under Hybrid Representations , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  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).

[19]  Eric Brachmann,et al.  Learning Analysis-by-Synthesis for 6D Pose Estimation in RGB-D Images , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[20]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[21]  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).

[22]  Yi Li,et al.  DeepIM: Deep Iterative Matching for 6D Pose Estimation , 2018, International Journal of Computer Vision.

[23]  Ross B. Girshick,et al.  Focal Loss for Dense Object Detection , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Yunsong Li,et al.  Minimum barrier superpixel segmentation , 2018, Image Vis. Comput..

[25]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[26]  Pascal Fua,et al.  Single-Stage 6D Object Pose Estimation , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[27]  Adrien Bartoli,et al.  Coarse-to-fine low-rank structure-from-motion , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[28]  John Leif Jørgensen,et al.  Pose estimation of an uncooperative spacecraft from actual space imagery , 2014 .

[29]  Yunsong Li,et al.  Efficient Coarse-to-Fine Patch Match for Large Displacement Optical Flow , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[30]  Shifeng Zhang,et al.  Bridging the Gap Between Anchor-Based and Anchor-Free Detection via Adaptive Training Sample Selection , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[31]  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).

[32]  Kaiming He,et al.  Focal Loss for Dense Object Detection , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

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

[34]  Yingli Tian,et al.  Coarse-to-Fine Semantic Segmentation From Image-Level Labels , 2018, IEEE Transactions on Image Processing.

[35]  Eric Brachmann,et al.  iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects , 2017, ACCV.

[36]  Andrew Zisserman,et al.  Spatial Transformer Networks , 2015, NIPS.

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

[38]  Hao Chen,et al.  FCOS: Fully Convolutional One-Stage Object Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[39]  Ross B. Girshick,et al.  Fast R-CNN , 2015, 1504.08083.

[40]  Xibin Cao,et al.  Closed‐form solution of monocular vision‐based relative pose determination for RVD spacecrafts , 2005 .

[41]  Xiaowei Zhou,et al.  6-DoF object pose from semantic keypoints , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[42]  Tat-Jun Chin,et al.  Satellite Pose Estimation with Deep Landmark Regression and Nonlinear Pose Refinement , 2019, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).

[43]  Slobodan Ilic,et al.  DPOD: 6D Pose Object Detector and Refiner , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[44]  Pascal Fua,et al.  Segmentation-Driven 6D Object Pose Estimation , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).