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.

Horyon 是一种基于开放词汇量的 Vision-Language Model 架构，通过使用自然语言描述来解决视觉中六维位姿估计任务中未见过对象之间的相对位姿估计问题，并在四个数据集上实现了最先进的性能，优于之前的方法。