  The practicality of 3D object pose estimation remains limited for many
applications due to the need for prior knowledge of a 3D model and a training
period for new objects. To address this limitation, we propose an approach that
takes a single image of a new object as input and predicts the relative pose of
this object in new images without prior knowledge of the object's 3D model and
without requiring training time for new objects and categories. We achieve this
by training a model to directly predict discriminative embeddings for
viewpoints surrounding the object. This prediction is done using a simple U-Net
architecture with attention and conditioned on the desired pose, which yields
extremely fast inference. We compare our approach to state-of-the-art methods
and show it outperforms them both in terms of accuracy and robustness. Our
source code is publicly available at https://github.com/nv-nguyen/nope
