A Feature-Enhancement 6D Pose Estimation Method for Weakly Textured and Occluded Targets

To achieve real-time and accurate pose estimation for weakly textured or occluded targets, this study proposes a feature-enhancement 6D pose estimation method based on DenseFusion. Firstly, in the image feature extraction stage, skip connections and attention modules, which could effectively fuse deep and shallow features, are introduced to enhance the richness and effectiveness of image features. Secondly, in the point cloud feature extraction stage, PointNet is applied to the initial feature extraction of the point cloud. Then, the K-nearest neighbor method and the Pool globalization method are applied to obtain richer point cloud features. Subsequently, in the dense feature fusion stage, an adaptive feature selection module is introduced to further preserve and enhance effective features. Finally, we add a supervision network to the original pose estimation network to enhance the training results. The results of the experiment show that the improved method performs significantly better than classic methods in both the LineMOD dataset and Occlusion LineMOD dataset, and all enhancements improve the real-time performance and accuracy of pose estimation.