Open-vocabulary object 6D pose estimation
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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 handcrafted 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.