Prior-information-guided corresponding point regression network for 6D pose estimation

Abstract

A key step in bin-picking is to obtain the 6DoF pose of the object. However, One of the reasons why many pose estimation methods fail to deal with occlusions and noise is that they lack reliable correspondence and effective utilization of CAD model information. In order to solve such issue, we propose a new prior-information-guided 6D pose estimation network based on 3D-3D correspondence prediction. Specifically, the network establishes dense correspondences between the object and the CAD model by predicting the object’s corresponding points on the CAD point cloud. This correspondence prediction method allows the network to learn more geometric details and perform better than direct regression. Furthermore, we implement the embedding of CAD model features in the network, which utilizes the prior information of the CAD model to compensate for the missing object information due to occlusion and viewpoint and provide more precise point regression. In addition, thanks to this points regression method, we can intuitively eliminate erroneous points via distance threshold judgment, thus achieving robustness to noise and outliers. Finally, the pose are then estimated through a SVD solver. Experiments show that our method outperforms most methods on LM, LM-O and YCB-V datasets, and its performance on LM-O datasets is 9% higher than the most advanced method. This proves that our method has excellent robustness and achieves the state-of-the-art performance.