Automation of "Ground Truth" Annotation for Multi-View RGB-D Object Instance Recognition Datasets

作者：Aldoma Aitor^*; Faeulhammer Thomas; Vincze Markus

来源：IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2014-09-14 to 2014-09-18.

Aiming at reducing the labour intensity associated with the acquisition of ground truth annotations for object instance recognition datasets, this paper discusses a novel multiview recognition method to automate the annotation (object instances and associated poses) of individual images in multiview RGB-D datasets. In combination with recent single-view object recognition techniques, the supplementary information provided by multiple vantage points results in a rich and integrated representation of the environment, in the form of a 3D reconstructed scene as well as object hypotheses therein. We argue that such a representation facilitates improved recognition to an extent that the recovered results, obtained by means of a suitable 3D hypotheses verification stage, closely resemble the ground truth of the scene under consideration. On two large datasets, totalling more than 3500 object instances, our method yields 99.1% and 93.2% correct automatic annotations. These results corroborate our approach for the task at hand.

• 出版日期2014