ZS6D: Zero-shot 6D Object Pose Estimation using Vision Transformers

Understand

As robotic systems increasingly encounter complex and unconstrained real-world scenarios, there is a demand to recognize diverse objects.

• The state-of-the-art 6D object pose estimation methods rely on object-specific training and therefore do not generalize to unseen objects.
• Recent novel object pose estimation methods are solving this issue using task-specific fine-tuned CNNs for deep template matching.
• This adaptation for pose estimation still requires expensive data rendering and training procedures.