  We present a novel one-shot method for object detection and 6 DoF pose
estimation, that does not require training on target objects. At test time, it
takes as input a target image and a textured 3D query model. The core idea is
to represent a 3D model with a number of 2D templates rendered from different
viewpoints. This enables CNN-based direct dense feature extraction and
matching. The object is first localized in 2D, then its approximate viewpoint
is estimated, followed by dense 2D-3D correspondence prediction. The final pose
is computed with PnP. We evaluate the method on LineMOD, Occlusion, Homebrewed,
YCB-V and TLESS datasets and report very competitive performance in comparison
to the state-of-the-art methods trained on synthetic data, even though our
method is not trained on the object models used for testing.
