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

TL;DR

PoET is a transformer-based method that estimates 6D object poses from single RGB images without requiring depth or 3D models, achieving state-of-the-art results on challenging datasets.

Contribution

This paper introduces a novel transformer-based approach for 6D pose estimation using only RGB images, eliminating the need for additional depth or 3D model data.

Findings

Achieves state-of-the-art results on YCB-V dataset for RGB-only pose estimation.

Effective handling of object symmetries, clutter, and occlusion.

Demonstrates suitability as a pose sensor for 6-DoF state estimation.

Abstract

Accurate 6D object pose estimation is an important task for a variety of robotic applications such as grasping or localization. It is a challenging task due to object symmetries, clutter and occlusion, but it becomes more challenging when additional information, such as depth and 3D models, is not provided. We present a transformer-based approach that takes an RGB image as input 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 or 3D object models. First, the image is passed through an object detector to generate feature maps and to detect objects. Then, the feature maps are fed into a transformer with the detected bounding boxes as additional information. Afterwards, the output object queries are processed by a separate translation and rotation head. We achieve state-of-the-art results for RGB-only approaches on the challenging YCB-V dataset. We illustrate the suitability of the resulting model as pose sensor for a 6-DoF state estimation task. Code is available at https://github.com/aau-cns/poet.