  The generalisation to unseen objects in the 6D pose estimation task is very
challenging. While Vision-Language Models (VLMs) enable using natural language
descriptions to support 6D pose estimation of unseen objects, these solutions
underperform compared to model-based methods. In this work we present Horyon,
an open-vocabulary VLM-based architecture that addresses relative pose
estimation between two scenes of an unseen object, described by a textual
prompt only. We use the textual prompt to identify the unseen object in the
scenes and then obtain high-resolution multi-scale features. These features are
used to extract cross-scene matches for registration. We evaluate our model on
a benchmark with a large variety of unseen objects across four datasets, namely
REAL275, Toyota-Light, Linemod, and YCB-Video. Our method achieves
state-of-the-art performance on all datasets, outperforming by 12.6 in Average
Recall the previous best-performing approach.
