GCE-Pose: Global Context Enhancement for Category-level Object Pose Estimation

• URL: http://arxiv.org/abs/2502.04293v1
• Date: Thu, 06 Feb 2025 18:35:13 GMT
• Title: GCE-Pose: Global Context Enhancement for Category-level Object Pose Estimation
• Authors: Weihang Li, Hongli Xu, Junwen Huang, Hyunjun Jung, Peter KT Yu, Nassir Navab, Benjamin Busam,
• Abstract summary: 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.<n>We present GCE-Pose, a method that enhances pose estimation for novel instances by integrating category-level global context prior.
• Score: 52.910282443646864
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. Our project page is available at https://colin-de.github.io/GCE-Pose/.

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