GCE-Pose: Global Context Enhancement for Category-Level Object Pose Estimation
MCML Authors
Nassir Navab
Prof. Dr.
Principal Investigator
Benjamin Busam
Prof. Dr.
Principal Investigator
Abstract
Nassir Navab
Prof. Dr.
Principal Investigator
Benjamin Busam
Prof. Dr.
Principal Investigator
Abstract
A key challenge in model-free category-level pose estimation is the extraction of contextual object features that generalize across varying instances within a specific category. Recent approaches leverage foundational features to capture semantic and geometry cues from data. However, these approaches fail under partial visibility. We overcome this with a first-complete-then-aggregate strategy for feature extraction utilizing class priors. In this paper, we present GCE-Pose, a method that enhances pose estimation for novel instances by integrating category-level global context prior. GCE-Pose performs semantic shape reconstruction with a proposed Semantic Shape Reconstruction (SSR) module. Given an unseen partial RGB-D object instance, our SSR module reconstructs the instance's global geometry and semantics by deforming category-specific 3D semantic prototypes through a learned deep Linear Shape Model. We further introduce a Global Context Enhanced (GCE) feature fusion module that effectively fuses features from partial RGB-D observations and the reconstructed global context. Extensive experiments validate the impact of our global context prior and the effectiveness of the GCE fusion module, demonstrating that GCE-Pose significantly outperforms existing methods on challenging real-world datasets HouseCat6D and NOCS-REAL275.
inproceedings
CVPR 2025
IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville, TN, USA, Jun 11-15, 2025.
Authors
W. Li • H. Xu • J. Huang • H. Jung • P. K. Yu • N. Navab • B. BusamLinks
DOI GitHubResearch Areas
BibTeXKey: LXH+25