High-resolution open-vocabulary object 6D pose estimation
<|reference_start|>High-resolution open-vocabulary object 6D pose estimation: 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.<|reference_end|>
@article{corsetti2024high-resolution,
    title={High-resolution open-vocabulary object 6D pose estimation},
    author={Jaime Corsetti, Davide Boscaini, Francesco Giuliari, Changjae Oh,
  Andrea Cavallaro, Fabio Poiesi},
    journal={arXiv preprint arXiv:2406.16384},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.16384},
    primaryClass={cs.CV}
}