Pov9D: Point Cloud-Based Open-Vocabulary 9D Object Pose Estimation

We propose a point cloud-based framework for open-vocabulary object pose estimation, called Pov9D. Existing approaches are predominantly RGB-based and often rely on texture or appearance cues, making them susceptible to pose ambiguities when objects are textureless or lack distinctive visual features. In contrast, Pov9D takes 3D point clouds as input, enabling direct access to geometric structures that are essential for accurate and robust pose estimation, especially in open-vocabulary settings. To bridge the gap between geometric observations and semantic understanding, Pov9D integrates category-level textual descriptions to guide the estimation process. To this end, we introduce a text-conditioned shape prior generator that predicts a normalized object shape from both the observed point cloud and the textual category description. This shape prior provides a consistent geometric reference, facilitating precise prediction of object translation, rotation, and size, even for unseen categories. Extensive experiments on the OO3D-9D benchmark demonstrate that Pov9D achieves state-of-the-art performance, improving Abs IoU@50 by 7.2% and Rel 10° 10 cm by 27.2% over OV9D.