AttentionPose: Attention-driven end-to-end model for precise 6D pose estimation

Abstract

Abstract Addressing the complex problem of 6D pose estimation from single RGB images is essential for robotics, augmented reality, and autonomous driving applications. The aim of this study is to overcome limitations in handling scenes with high object occlusion and clutter. We introduce an attention-driven end-to-end model that builds upon existing methods employing pixel-wise unit vectors and voting for object keypoints. Integrating attention mechanisms allows the model to focus computational resources on salient features, enhancing accuracy. Experimental results using the LINEMOD benchmark dataset demonstrate an accuracy rate of 99.73%, outperforming state-of-the-art approaches. The model also exhibits strong generalization capabilities, achieving an average accuracy of 97.36% on objects not included in the dataset. This work concludes that the attention mechanism significantly elevates the performance and robustness of 6D pose estimation, particularly in challenging environments, and opens new avenues for real-world applications.