  6D object pose estimation is a crucial prerequisite for autonomous robot
manipulation applications. The state-of-the-art models for pose estimation are
convolutional neural network (CNN)-based. Lately, Transformers, an architecture
originally proposed for natural language processing, is achieving
state-of-the-art results in many computer vision tasks as well. Equipped with
the multi-head self-attention mechanism, Transformers enable simple
single-stage end-to-end architectures for learning object detection and 6D
object pose estimation jointly. In this work, we propose YOLOPose (short form
for You Only Look Once Pose estimation), a Transformer-based multi-object 6D
pose estimation method based on keypoint regression. In contrast to the
standard heatmaps for predicting keypoints in an image, we directly regress the
keypoints. Additionally, we employ a learnable orientation estimation module to
predict the orientation from the keypoints. Along with a separate translation
estimation module, our model is end-to-end differentiable. Our method is
suitable for real-time applications and achieves results comparable to
state-of-the-art methods.
