  We present a method that can recognize new objects and estimate their 3D pose
in RGB images even under partial occlusions. Our method requires neither a
training phase on these objects nor real images depicting them, only their CAD
models. It relies on a small set of training objects to learn local object
representations, which allow us to locally match the input image to a set of
"templates", rendered images of the CAD models for the new objects. In contrast
with the state-of-the-art methods, the new objects on which our method is
applied can be very different from the training objects. As a result, we are
the first to show generalization without retraining on the LINEMOD and
Occlusion-LINEMOD datasets. Our analysis of the failure modes of previous
template-based approaches further confirms the benefits of local features for
template matching. We outperform the state-of-the-art template matching methods
on the LINEMOD, Occlusion-LINEMOD and T-LESS datasets. Our source code and data
are publicly available at https://github.com/nv-nguyen/template-pose
