SANet: A novel segmented attention mechanism and multi-level information fusion network for 6D estimation

Reliably and rapidly estimating the 6D position of an object is a critical challenge when using Internet of Things (IoT) technologies for monitoring. Nowadays, the prevalent 6D pose estimation architecture is based on a two -stage technique, which requires a significant amount of time for both training and deploying the algorithm in actual applications. Additionally, the majority of approaches include intricate high-low level features in the network that greatly influence training but contribute less to testing. To enable more accurate 6D object pose estimation while shortening the deployment time, we used a single-stage end-to-end algorithm to design the network. In this paper, we propose SANet, which is composed of a segmented attention module and a multi-level information fusion module. Specifically, by extracting high-level semantic information from images before fusing them to the decoder, and by removing redundant information using the multi-level information fusion module, the feature fusion complexity of the model is reduced by extracting high level features. In addition, the segmented attention module can suppress unreliable information to enhance network learning of channel and spatial information, enabling the network to more accurately understand the geometric aspects of the object. Extensive experiments on LM and LMO datasets demonstrate that our method outperforms state-of-the-art baselines, ranking 1st in both speed and accuracy.