# MPCR-Net: Multiple Partial Point Clouds Registration Network Using a Global Template
## 2 Related Work
### Deep Learning-Based Registration Algorithms  
Recent studies have shown that point cloud registration algorithms based on deep learning have higher registration accuracy than classic point cloud registration algorithms [26; 27; 28; 29; 30].  
[PERSON] et al. [10] proposed an end-to-end deep neural network (PointNet) that could directly take point clouds as a network input. PointNet overcomes the shortcomings of general deep learning methods that cannot effectively extract features from unstructured point clouds, and it establishes a learnable structured representation method for unstructured point clouds. PointNet and its variants have been successfully applied in point cloud classification, object detection [31], and point cloud completion tasks [32].  
In point cloud registration tasks, some deep learning algorithms use the PointNet architecture to obtain the learnable structural features of the unstructured point clouds,train rigid body transformation matrices for point cloud registration based on these features, and obtain satisfactory point cloud registration results.  
PointNetLK [33] is the first deep learning-based algorithm to use a learnable structured representation method for point cloud registration. PointNetLK modifies the classical Lucas and Kanade (LK) algorithm [34] to circumvent the inherent inability of the PointNet representation to accommodate gradient estimates through convolution. This modified LK framework is then unrolled as a recurrent neural network in which PointNet is integrated to construct the PointNetLK architecture. However, PointNetLK and similar algorithms, such as the DCP [35], DeepGMR [36], and PCRNet [37], work on the assumption that all the points in the point clouds are inliers by default. Naturally, they perform poorly when one of the point clouds has missing points, as in the case of partial point clouds.  
Unfortunately, in actual point cloud 3D reconstruction tasks, it is rare for the geometric structures of pairwise point clouds to be the same, especially for large workpieces. Each raw point cloud collected by the measuring device can only map the local structure of the workpiece.  
A class of point cloud registration algorithms, PRNet [38] and RPM-Net [39], which also contain the PointNet architecture, can handle partial-to-partial point cloud registration. Their application range is wider than that of PointNetLK and other algorithms that can only perform global point cloud registration. Unfortunately, these algorithms do not scale well when the number of points increases. If the number of points of the pairwise point clouds differs significantly, the estimation result of the rigid body transformation matrix will fluctuate with the number of points, resulting in a decrease in registration accuracy.  
Other deep learning-based algorithms, such as the DGR [40] and multi-view registration network [41], can use neural networks to filter out some outliers from the correspondence points of pairwise point clouds, but these algorithms require a clear correspondence between the pairwise point clouds.