THE-Pose: Topological Prior with Hybrid Graph Fusion for Estimating Category-Level 6D Object Pose

TL;DR

THE-Pose introduces a topological prior and hybrid graph fusion to improve category-level 6D object pose estimation, enhancing robustness against intra-class variations, occlusions, and complex geometries.

Contribution

It proposes a novel framework combining topological surface embedding with hybrid graph fusion to better integrate 2D and 3D features for pose estimation.

Findings

Achieves 35.8% improvement over 3D-GC baseline

Surpasses previous state-of-the-art by 7.2% on REAL275

Demonstrates robustness on complex and occluded objects

Abstract

Category-level object pose estimation requires both global context and local structure to ensure robustness against intra-class variations. However, 3D graph convolution (3D-GC) methods only focus on local geometry and depth information, making them vulnerable to complex objects and visual ambiguities. To address this, we present THE-Pose, a novel category-level 6D pose estimation framework that leverages a topological prior via surface embedding and hybrid graph fusion. Specifically, we extract consistent and invariant topological features from the image domain, effectively overcoming the limitations inherent in existing 3D-GC based methods. Our Hybrid Graph Fusion (HGF) module adaptively integrates the topological features with point-cloud features, seamlessly bridging 2D image context and 3D geometric structure. These fused features ensure stability for unseen or complicated objects, even under significant occlusions. Extensive experiments on the REAL275 dataset show that THE-Pose achieves a 35.8% improvement over the 3D-GC baseline (HS-Pose) and surpasses the previous state-of-the-art by 7.2% across all key metrics. The code is avaialbe on https://github.com/EHxxx/THE-Pose