Effects of Markers in Training Datasets on the Accuracy of 6D Pose Estimation

Collecting training data for pose estimation methods on images is a time-consuming task and usually involves some kind of manual labeling of the 6D pose of objects. This time could be reduced considerably by using marker-based tracking that would allow for automatic labeling of training images. However, images containing markers may reduce the accuracy of pose estimation due to a bias introduced by the markers. In this paper, we analyze the influence of markers in training images on pose estimation accuracy. We investigate the accuracy of estimated poses for three different cases: i) training on images with markers, ii) removing markers by inpainting, and iii) augmenting the dataset with randomly generated markers to reduce spatial learning of marker features. Our results demonstrate that utilizing marker-based techniques is an effective strategy for collecting large amounts of ground truth data for pose prediction. Moreover, our findings suggest that the usage of inpainting techniques do not reduce prediction accuracy. Additionally, we investigate the effect of inaccuracies of labeling in training data on prediction accuracy. We show that the precise ground truth data obtained through marker tracking proves to be superior compared to markerless datasets if labeling errors of 6D ground truth exist. Our data generation tools are available online: https://github.com/JHRosskamp/6DPoseDataGenTools