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Accurate 6D object pose estimation is an important task for a variety ofrobotic applications such as grasping or localization. It is a challenging taskdue to object symmetries, clutter and occlusion, but it becomes morechallenging when additional information, such as depth and 3D models, is notprovided. We present a transformer-based approach that takes an RGB image asinput and predicts a 6D pose for each object in the image. Besides the image,our network does not require any additional information such as depth maps or3D object models. First, the image is passed through an object detector togenerate feature maps and to detect objects. Then, the feature maps are fedinto a transformer with the detected bounding boxes as additional information.Afterwards, the output object queries are processed by a separate translationand rotation head. We achieve state-of-the-art results for RGB-only approacheson the challenging YCB-V dataset. We illustrate the suitability of theresulting model as pose sensor for a 6-DoF state estimation task. Code isavailable at https://github.com/aau-cns/poet.

PoET: Pose Estimation Transformer for Single-View, Multi-Object 6D Pose Estimation