Local features of 3D point cloud registration based on Siamese network learning

Abstract

ABSTRACT To solve the problem of point cloud registration caused by noisy or incomplete data, a deep learning model based on the 3D Siamese convolution neural network is proposed. The key idea of our algorithm is the acquisition of the training data. To accumulate training data for our model, we project 3D point cloud data onto a 2D plane to obtain cumulative projection images, then we use the SIFT algorithm to match the images and obtain the matching point pairs. On this basis, according to the relationship between the matching point pairs and 3D point cloud, the 3D point blocks are obtained, and finally the Siamese convolution neural network is used to learn the 3D point block pairs and obtain the 3D feature descriptor. The point cloud can be effectively matched by using the feature descriptor and Random Sample Consensus (RANSAC) algorithm. Experiments show that the descriptor consistently outperforms other state-of-the-art approaches by a significant margin; therefore, this method offers significant improvement in processing speed and registration accuracy.