Semi-supervised Deep Closest Point Method for Point Cloud Registration

Abstract Point cloud registration is one of the key issues in fields that need 3D scenes with global vision, including 3D scene reconstruction in robot technology, high-accuracy 3D map reconstruction in automatic driving, 3D reconstruction of real-time monitoring underground tunnel. Recently, point cloud registration methods based on deep neural networks have made great progress by learning from a large amount of well-aligned point cloud pairs. However, these large-scale labelled samples are usually difficult to obtain in the real-world applications such as visual simultaneous location and mapping of mobile robots which obtains unlabelled scene images in real time. To solve this problem, we propose a deep point cloud registration method based on the semi-supervised learning. Specifically, for the problem of less supervised data, we use Iterative Closest Point (ICP) algorithm to generate pseudo labels for the point cloud samples with unknown rigid transformations, effectively increasing the number of supervised data; in order to amend the pseudo labels, we design an alternating optimization algorithm to jointly learn the pseudo labels and deep point cloud registration model. The deep point cloud registration model and ICP are used alternately to continuously improve the precision of pseudo labels and the performance of the deep model. Experimental results show that this method can still obtain a reliable point cloud registration model even when just observing a small amount of point cloud samples with ground truth labels.