主催: The Japan Society of Mechanical Engineers
会議名: ロボティクス・メカトロニクス 講演会2020
開催日: 2020/05/27 - 2020/05/30
This paper presents a convenient, semi-automated pipeline for the annotation of real-world RGB-D data with pose ground truths of known objects using minimal input from the human user. This method enables rapid generation of labeled datasets for the training of CNN-based pose estimation of the objects desired in the user’s particular application.