  In the current state of 6D pose estimation, top-performing techniques depend
on complex intermediate correspondences, specialized architectures, and
non-end-to-end algorithms. In contrast, our research reframes the problem as a
straightforward regression task by exploring the capabilities of Vision
Transformers for direct 6D pose estimation through a tailored use of
classification tokens. We also introduce a simple method for determining pose
confidence, which can be readily integrated into most 6D pose estimation
frameworks. This involves modifying the transformer architecture by decreasing
the number of query elements based on the network's assessment of the scene
complexity. Our method that we call Pose Vision Transformer or PViT-6D provides
the benefits of simple implementation and being end-to-end learnable while
outperforming current state-of-the-art methods by +0.3% ADD(-S) on
Linemod-Occlusion and +2.7% ADD(-S) on the YCB-V dataset. Moreover, our method
enhances both the model's interpretability and the reliability of its
performance during inference.
