  Template matching is a fundamental task in computer vision and has been
studied for decades. It plays an essential role in manufacturing industry for
estimating the poses of different parts, facilitating downstream tasks such as
robotic grasping. Existing methods fail when the template and source images
have different modalities, cluttered backgrounds or weak textures. They also
rarely consider geometric transformations via homographies, which commonly
exist even for planar industrial parts. To tackle the challenges, we propose an
accurate template matching method based on differentiable coarse-to-fine
correspondence refinement. We use an edge-aware module to overcome the domain
gap between the mask template and the grayscale image, allowing robust
matching. An initial warp is estimated using coarse correspondences based on
novel structure-aware information provided by transformers. This initial
alignment is passed to a refinement network using references and aligned images
to obtain sub-pixel level correspondences which are used to give the final
geometric transformation. Extensive evaluation shows that our method is
significantly better than state-of-the-art methods and baselines, providing
good generalization ability and visually plausible results even on unseen real
data.
