Enhanced YOLOv11 with global geometric perception for 6D object pose estimation
TL;DR
This study presents an enhanced YOLOv11 framework specifically designed for global geometric perception and high-fidelity single-stage 6D pose regression, which validate that the integration of global geometric awareness consistently outperforms the vanilla YOLOv11 and other classical baselines in complex scenarios.
Abstract
Precise 6D object pose estimation from RGB images remains a formidable challenge due to complex backgrounds and severe occlusions. To address these issues, our study presents an enhanced YOLOv11 framework specifically designed for global geometric perception and high-fidelity single-stage 6D pose regression. The core of our architecture is the C3k2SW module, which innovatively synergizes local convolutional features with global long-range dependencies through windowbased self-attention, significantly enhancing the network's geometric perception of spatial topologies. Furthermore, to optimize multi-scale feature interaction, an adaptive ConcatA module and a Bi-directional Feature Pyramid Attention Network (BFPAN) are proposed to suppress background noise while preserving fine-grained geometric details across different scales. Experimental results on the LineMod benchmark demonstrate that our method achieves an optimal tradeoff between inference efficiency and accuracy, reaching an average ADD(-S) accuracy of 76.50% and 84.92% on the 5cm 5° metric, respectively. These results validate that the integration of global geometric awareness consistently outperforms the vanilla YOLOv11 and other classical baselines in complex scenarios.