This paper discusses an edge-direction-based template matching algorithm that allows to detect industrial objects
despite perspective distortion. We construct a deformable template by decomposing a shape model into independent
parts, where the deformation is restricted to, e.g., a homography. The deformable template in combination
with a coarse-to-fine strategy allows to overcome the speed limitations of an exhaustive template matching of a
3D search range. The relevant size of the model that is used for the search at the highest pyramid level is not
reduced. Therefore, we do not suffer the speed limitations that prior methods have. Furthermore, enforcing a
consistent polarity in each part, but ignoring different polarities between different parts allows us to efficiently
and robustly detect untextured metallic objects that are encountered in typical factory automation scenarios.
Finally, we present results of an experimental evaluation with respect to speed, robustness and accuracy.