Real-time 3D Pose Estimation from Single Depth Images

Abstract

To allow for safe Human-Robot-Interaction in industrial scenarios like manufacturing plants, it is essential to always be aware of the location and pose of humans in the shared workspace. We introduce a real-time 3D pose estimation system using single depth images that is aimed to run on limited hardware, such as a mobile robot. For this, we optimized a CNN-based 2D pose estimation architecture to achieve high frame rates while simultaneously requiring fewer resources. Building upon this architecture, we extended the system for 3D estimation to directly predict Cartesian body joint coordinates. We evaluated our system on a newly created dataset by applying it to a specific industrial workbench scenario. The results show that our system’s performance is competitive to the state of the art at more than five times the speed for single person pose estimation.