3D Human Pose Estimation in RGBD Images for Robotic Task Learning

March 07, 2018 · Entered Twilight · 🏛 IEEE International Conference on Robotics and Automation

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Repo contents: PoseNet3D.py, README.md, RosNode.py, color.png, depth.png, forward_pass.py, nets, teaser.png, utils

Authors Christian Zimmermann, Tim Welschehold, Christian Dornhege, Wolfram Burgard, Thomas Brox arXiv ID 1803.02622 Category cs.CV: Computer Vision Cross-listed cs.RO Citations 171 Venue IEEE International Conference on Robotics and Automation Repository https://github.com/lmb-freiburg/rgbd-pose3d ⭐ 211 Last Checked 1 month ago

Abstract

We propose an approach to estimate 3D human pose in real world units from a single RGBD image and show that it exceeds performance of monocular 3D pose estimation approaches from color as well as pose estimation exclusively from depth. Our approach builds on robust human keypoint detectors for color images and incorporates depth for lifting into 3D. We combine the system with our learning from demonstration framework to instruct a service robot without the need of markers. Experiments in real world settings demonstrate that our approach enables a PR2 robot to imitate manipulation actions observed from a human teacher.