RFF-PoseNet: A 6D Object Pose Estimation Network Based on Robust Feature Fusion in Complex Scenes
Year: 2024
Type: article
Abstract: Six degrees-of-freedom (6D) object pose estimation plays an important role in pattern recognition of fields such as robotics and augmented reality. However, there are issues with low accuracy and real-time performance of 6D object pose estimation in complex scenes. To address these challenges, in this article, RFF-PoseNet (a 6D object pose estimation network based on robust feature fusion) is proposed for complex scenes. Firstly, a more lightweight Ghost module is used to replace the convolutional blocks in the feature extraction network. Then, a pyramid pooling module is added to the semantic label branch of PoseCNN to fuse the features of different pooling layers and enhance the network’s ability to capture information about objects in complex scenes and the correlations between contextual information. Finally, a pose regression and optimization module is utilized to further improve object pose estimation in complex scenes. Simulation experiments conducted on the YCB-Video and Occlusion LineMOD datasets show that the RFF-PoseNet algorithm can strengthen the correlation of features between different levels and the recognition ability of unclear targets, thereby achieving excellent accuracy and real-time performance, as well as strong robustness.
Source: Electronics
Authors: Xiaomei Lei, Wenhuan Lu, Jiu Yong, Jianguo Wei
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,728
OpenAlex ID: W4402281992