  Object pose estimation is important for object manipulation and scene
understanding. In order to improve the general applicability of pose
estimators, recent research focuses on providing estimates for novel objects,
that is objects unseen during training. Such works use deep template matching
strategies to retrieve the closest template connected to a query image. This
template retrieval implicitly provides object class and pose. Despite the
recent success and improvements of Vision Transformers over CNNs for many
vision tasks, the state of the art uses CNN-based approaches for novel object
pose estimation. This work evaluates and demonstrates the differences between
self-supervised CNNs and Vision Transformers for deep template matching. In
detail, both types of approaches are trained using contrastive learning to
match training images against rendered templates of isolated objects. At test
time, such templates are matched against query images of known and novel
objects under challenging settings, such as clutter, occlusion and object
symmetries, using masked cosine similarity. The presented results not only
demonstrate that Vision Transformers improve in matching accuracy over CNNs,
but also that for some cases pre-trained Vision Transformers do not need
fine-tuning to do so. Furthermore, we highlight the differences in optimization
and network architecture when comparing these two types of network for deep
template matching.
