RYOPO: Bringing End-to-End Category-Level Object Pose Estimation into Real Time

Abstract:Category-level object pose estimation predicts the rotation, translation, and metric size of unseen instances within known categories. Many accurate RGB-D methods rely on external instance segmentation and crop-based pose estimation, introducing separate stages and object-dependent processing costs that hinder real-time inference. To bring accurate pose estimation into real time, we present \ours{}, an end-to-end trainable query-based RGB-D set predictor. It jointly detects and segments objects and estimates their \mbox{9-DoF} poses without explicit CAD-derived shape priors or a separately trained instance segmentor. Shared image and scene encoding avoids repeated per-object crop encoding. A query-conditioned geometry pathway associates observed 3D points and RGB features with object queries and incorporates shared scene context. Object-centric refinement uses the resulting point descriptors to update an explicit pose state through pose-conditioned cross-attention and recurrent residual corrections. On NOCS, \ours{} substantially improves on published RGB-D joint detection and pose estimation results. It achieves competitive performance compared with two-stage methods under all-object evaluation on REAL275 and HouseCat6D, while enabling real-time full-frame pose estimation at $31.8$ FPS on an RTX~A6000. Project page: this https URL.

Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)

arXiv:2610.03013 [cs.CV]

(or arXiv:2610.03013v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2610.03013 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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