2020/06/14 by Myung–Hwan Jeon, Myung-Hwan Jeon, Jeon, Myung-Hwan +2
Computer Science · Engineering · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image and Object Detection Techniques #Robot Manipulation and Learning #Robotics (cs.RO) #Robotics and Sensor-Based Localization #cs.CV #cs.RO
paper · pdf · doi:10.48550/arxiv.2006.07789
RA-L and IROS 2020
openalex publication_date 2020/06/14 · arxiv created 2020/07/03 · arxiv updated 2020/07/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we introduce a rotational primitive prediction based 6D object pose estimation using a single image as an input. We solve for the 6D object pose of a known object relative to the camera using a single image with occlusion. Many recent state-of-the-art (SOTA) two-step approaches have exploited image keypoints extraction followed by PnP regression for pose estimation. Instead of relying on bounding box or keypoints on the object, we propose to learn orientation-induced primitive so as to achieve the pose estimation accuracy regardless of the object size. We leverage a Variational AutoEncoder (VAE) to learn this underlying primitive and its associated keypoints. The keypoints inferred from the reconstructed primitive image are then used to regress the rotation using PnP. Lastly, we compute the translation in a separate localization module to complete the entire 6D pose estimation. When evaluated over public datasets, the proposed method yields a notable improvement over the LINEMOD, the Occlusion LINEMOD, and the YCB-Video dataset. We further provide a synthetic-only trained case presenting comparable performance to the existing methods which require real images in the training phase.