# ALReg: Registration of 3D Point Clouds Using Active Learning
## 1 Introduction  
In recent years, point cloud classification and segmentation methods using deep learning have dominated the field. The pioneer work in the field is the PointNet [1], in which the point positions are fed directly to the network. After PointNet's success, many other methods handled the problem using a deep learning approach [2; 3; 4]. Despite the mentioned studies feeding a whole point cloud to the network in the training process, it is shown that not all points in a point cloud have the same contribution to the output of the network [5], and using a relatively smaller amount of points, similar results could be obtained [6]. Thus, especially for large point cloud sets, an active learning-based approach could be adopted to only use a certain count of patches in the training procedure [7; 8; 9; 10]. A keypoint-based approach is followed, which also utilizes a relatively smaller number of points while matching the point clouds [3; 11]. Point cloud registration, which could be defined as the calculation of the rigid transformation between multiple point clouds and aligning them, is an important problem for many computer vision tasks in robotics, autonomous driving, and 3D reconstruction [12]. The traditional methods, like Iterative Closest Point (ICP) [13] and other methods considering point-to-point or point-to-plane matching using only mathematical constraints [14; 15; 16], have been replaced by learning-based methods [3; 17; 18]. Using the deep feature representation of each point or local patch of the point cloud, feature-based metrics are involved togain more robust matching against outliers and noises. Even though point cloud registration has achieved significant progress using the previously mentioned methods, most of them (even in the partial-to-partial point cloud registration [19; 20; 21]) are focused on the use of the whole shape to train the network for registration. However, similarly, this problem could also be handled using active learning which was not been addressed in the literature. Active learning is substantially useful where data labeling is costly, and hence, only a subset of the data ought to be used in the training process. Thus, the most discriminative and diverse subset of the data should be selected. Initially, the training is started with a very small subset of the training data and, using the active learning pipeline, this subset is expanded. A simplified active learning pipeline could be considered as a three-part system: A _predictor_, an _oracle_, and a _guide_[22]. In the common approach, the predictor is a parametric machine learning model (e.g., a deep neural network) which is trained for the task using the labeled data (despite the traditional denomination in segmentation and classification problems, the term labeled data here refers to the data engaged in the training procedure). The guide calculates an acquisition score for each unlabeled data using a heuristic function and/or the feedback obtained from the predictor. This feedback could be simply the uncertainty/discrepancy amount of each unlabeled sample, since a sample for which the network could not reach a decision certainly, will have an impact on the construction of the network when included in the training procedure. Finally, the oracle uses the Guide's scores and labels some of the unlabeled data according to a policy. Instead of selecting samples from the training set, in the 2D image segmentation methods utilizing active learning [23; 24; 25], some parts of each image could also be selected. Considering that the labeling cost of 3D segmentation is higher than its 2D counterparts, some efforts have been devoted to using active learning in point cloud segmentation [26; 27; 28]. These studies adopted the superpoint [29] representation where each point cloud is divided into many superpoints using an over-segmentation approach and labeled superpoints in the active learning pipeline. A similar approach could be applied to point cloud registration.