YOLOPose V2: Understanding and Improving Transformer-based 6D Pose
  Estimation
Arul Selvam Periyasamy Arash Amini Vladimir Tsaturyan Sven Behnke
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 and an improved variant of
the YOLOPose model. 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. We
analyze the role of object queries in our architecture and reveal that the
object queries specialize in detecting objects in specific image regions.
Furthermore, we quantify the accuracy trade-off of using datasets of smaller
sizes to train our model.