  We introduce the new setting of open-vocabulary object 6D pose estimation, in
which a textual prompt is used to specify the object of interest. In contrast
to existing approaches, in our setting (i) the object of interest is specified
solely through the textual prompt, (ii) no object model (e.g., CAD or video
sequence) is required at inference, and (iii) the object is imaged from two
RGBD viewpoints of different scenes. To operate in this setting, we introduce a
novel approach that leverages a Vision-Language Model to segment the object of
interest from the scenes and to estimate its relative 6D pose. The key of our
approach is a carefully devised strategy to fuse object-level information
provided by the prompt with local image features, resulting in a feature space
that can generalize to novel concepts. We validate our approach on a new
benchmark based on two popular datasets, REAL275 and Toyota-Light, which
collectively encompass 34 object instances appearing in four thousand image
pairs. The results demonstrate that our approach outperforms both a
well-established hand-crafted method and a recent deep learning-based baseline
in estimating the relative 6D pose of objects in different scenes. Code and
dataset are available at https://jcorsetti.github.io/oryon.
