Object Pose Estimation Annotation Pipeline for Multi-view Monocular Camera Systems in Industrial Settings
This addresses the need for scalable data annotation in industrial object localization, though it is incremental as it builds on existing methods for camera localization and projection.
The paper tackles the problem of annotating large datasets for object pose estimation in industrial settings by automating the annotation process using camera localization and 3D model projection, resulting in consistent annotations for 26,482 object instances in significantly less time than manual labor.
Object localization, and more specifically object pose estimation, in large industrial spaces such as warehouses and production facilities, is essential for material flow operations. Traditional approaches rely on artificial artifacts installed in the environment or excessively expensive equipment, that is not suitable at scale. A more practical approach is to utilize existing cameras in such spaces in order to address the underlying pose estimation problem and to localize objects of interest. In order to leverage state-of-the-art methods in deep learning for object pose estimation, large amounts of data need to be collected and annotated. In this work, we provide an approach to the annotation of large datasets of monocular images without the need for manual labor. Our approach localizes cameras in space, unifies their location with a motion capture system, and uses a set of linear mappings to project 3D models of objects of interest at their ground truth 6D pose locations. We test our pipeline on a custom dataset collected from a system of eight cameras in an industrial setting that mimics the intended area of operation. Our approach was able to provide consistent quality annotations for our dataset with 26, 482 object instances at a fraction of the time required by human annotators.