# 3-D Point Cloud Registration Using Convolutional Neural Networks
## 2 Related Work  
Basically, point cloud registration consists of two stages including rough registration and precise registration stage for algorithms based on Iterative Closest Point (ICP) approach and its variants. In particular, the rough registration is performed to give an initial estimation about the transformation between two point clouds, followed by the precise registration to refine this estimation to best align these two point clouds. On the one hand, to address the rough registration problem, a well-known algorithm, RANdom Sample Consensus (RANSAC) approach, was proposed in Reference [PERSON] et al. (2018) to roughly estimate the rotation and translation between two point clouds. Because of slow computational speed, several variants of the RANSAC algorithm were developed to address this problem such as IMPSAC [PERSON] et al. (2018), Preemptive RANSAC [PERSON] et al. (2018), Distributed RANSAC [PERSON] et al. (2018) and Recursive-RANSAC [PERSON] et al. (2018). Besides, [PERSON] et al. [PERSON] et al. (2018) proposed an improved RANSAC algorithm based on 3-D Region Covariance (RC) descriptor, RC-RANSAC method, to enhance the successful percentage of point cloud registration in a single repetition when describing region covariance of each point. Additionally, other global registration algorithms in the case of arbitrary initial poses between two point clouds were also published in recent years. In Reference [PERSON] et al. (2018), [PERSON] et al. developed a robust and fast alignment principle, 4-Points Congruent Sets (4 PCS) algorithm, employing wide bases robust to outliers and noise for 3-D point clouds based on extracting all sets of coplanar four-points from a 3-D point cloud. Moreover, similarity and affine transform problems were taken into account in their research. However, a drawback existed in their method because 4 PCS approach took approximately a quadratic time complexity according to the quantity of data points. Hence, they presented an improvement of 4 PCS method, called Super 4 PCS approach [PERSON] et al. (2018), that degraded the quadratic complexity to linear complexity and confined the volume of candidate conjugate pairs created to tackle this negative point. Super 4 PCS approach appeared to be an effective solution to handle 3-D point cloud registration when existing a low overlap and outliers. The other existing approach, the Iterative Closest Point method integrated with the Normal Distribution Transform approach (NDT-ICP), was proposed in Reference [20] with the aim of accelerating the registration speed. In addition, [PERSON] et al. [21] developed a point cloud registration algorithm based on improved digital image correlation approach, followed by the ICP method with a high accuracy, anti-noise capability and efficiency. Moreover, [PERSON] et al. proposed the candidate-based axially switching (CBAS) computed closer point (CCP) algorithm [22] to effectively and robustly address rough registration problem in the case of an arbitrarily initial pose between two point clouds. On the other hand, as regards the precise registration fulfilled after the rough registration, the most common registration algorithm is the ICP approach proposed by [PERSON] et al. in Reference [23] that minimizes the distance between two point clouds. In addition, many variants of the ICP method were presented in recent years with the aim of improving the original method in some aspects such as high accuracy, high speed, robust registration, scale registration, affine registration and globally-optimal solution. Particularly, dual interpolating point-to-surface algorithm adopting surface fitting by using B-spline to establish correspondences between two point clouds was proposed in Reference [24] to address the precise registration for two point clouds with a partial overlap. [PERSON] et al. [25] developed the Hong-Tan based ICP method (HT-ICP) for the purpose of improving the speed and precision of registration for partially overlapping point clouds.