Object pose estimation is a fundamental task in computer vision and robotics, yet most methods require extensive, dataset-specific training. Concurrently, large-scale vision language models show remarkable zero-shot capabilities. In this work, we bridge these two worlds by introducing ConceptPose, a framework for object pose estimation that is both training-free and model-free. ConceptPose leverages a vision-language-model (VLM) to create open-vocabulary 3D concept maps, where each point is tagged with a concept vector derived from saliency maps. By establishing robust 3D-3D correspondences across concept maps, our approach allows precise estimation of 6DoF relative pose. Without any object or dataset-specific training, our approach achieves state-of-the-art results on common zero shot relative pose estimation benchmarks, outperforming the strongest baseline by a relative 62\% in average ADD(-S) score, including methods that utilize extensive dataset-specific training.
@article{arxiv.2512.09056,
title = {ConceptPose: Training-Free Zero-Shot Object Pose Estimation using Concept Vectors},
author = {Liming Kuang and Yordanka Velikova and Mahdi Saleh and Jan-Nico Zaech and Danda Pani Paudel and Benjamin Busam},
journal= {arXiv preprint arXiv:2512.09056},
year = {2026}
}