6D Pose 耘stimation of Low Texture Industrial Parts Based on Pseudo-Siamese Neural Network

Obtaining the 6D pose information of the target object from a single frame RGB image is widely used in the fields of robot capture, virtual reality, automatic driving, and so on. Aiming at the problem of insufficient accuracy of pose estimation of low texture objects, a pose estimation method based on pseudo-siamese neural network is proposed in this paper. Firstly, RGB images from different viewing angles are obtained as training samples by rendering CAD models, which solves the cumbersome problem of data set acquisition and annotation in deep learning. Secondly, the pseudo-siamese neural network structure is used to learn the similarity between the two-dimensional image features and the three-dimensional mesh model features of the object, that is, the full convolution network and the three-dimensional point cloud semantic segmentation network are used to form the pseudo-siamese neural network, extract the high-dimensional deep features of the two-dimensional image and the three-dimensional model, and use the network to infer the dense two-dimensional three-dimensional correspondence. Finally, the pose of the object is restored by PNP-RANSAC method. The experimental results of simulation data sets show that the proposed method has high accuracy and robustness. © 2023 Chinese Institute of Electronics. All rights reserved.