# MPCR-Net: Multiple Partial Point Clouds Registration Network Using a Global Template
## 3 MPCR-Net  
### Overview  
Most existing deep learning-based partial-to-partial point cloud registration algorithms iteratively perform registration between partial point clouds to realize the registration between multiple partial point clouds, before finally building a complete reconstruction model of the workpiece. However, when the overlapping area between the pairwise partial point clouds is small, the registration accuracy cannot be guaranteed, and a large 3D reconstruction error accumulates after the iterative registration of multiple partial point clouds. We propose MPCR-Net, a multiple partial point cloud registration network, which uses a global template to improve the registration accuracy of a large workpiece. In this network, the template point cloud extracted from a CAD model is used as the global registration template, and multiple partial point clouds are gradually pasted onto the global template; the relative poses of partial point clouds and the template point clouds are subsequently optimized to obtain a fully registered point cloud of all the partial point clouds. Compared with existing registration algorithms, the MPCR-Net can guarantee the overlap rate between the pairwise point clouds (the partial point cloud can be approximately regarded as a subset of the template point cloud), thus reducing the registration difficulty and error. MPCR-Net mainly comprises TPCC-Net and TMPE-Net. TPCC-Net uses a deep neural network to extract and merge the features of a partial point cloud and the global template point cloud; it then \"cuts out\" a correspondence partial template point cloud in the global template point cloud. TMPE-Net merges the features of partial point clouds and the correspondence partial template point clouds, then iteratively learns the optimal rigid body transformation matrix. As shown in Figure 1, the overall workflow of the MPCR-Net is as follows:Suppose there are n partial point clouds \(S_{1},\ldots,S_{n}\) and a global template point cloud \(T\), \(S_{1},\ldots,S_{n}\) and \(T\) are used as the inputs of TPCC-Net. In the TPCC-Net, the feature matrices \(F(S_{1}),\ldots,F(S_{n})\) of \(S_{1},\ldots,S_{n}\) and \(F(T)\) of \(T\) are obtained through the point cloud feature perceptron, respectively;
2. Global feature vectors \(\varnothing(S_{1}),\ldots,\varnothing(S_{n})\) are obtained by pooling \(F(S_{1}),\ldots,F(S_{n})\), then the fusion features \(F_{1},\ldots,F_{n}\) are obtained by splicing \(\varnothing(S_{1}),\ldots,\varnothing(S_{n})\) and \(F(T)\)[42];
3. Index features \(M_{1},\ldots,M_{n}\) of \(F_{1},\ldots,F_{n}\) are obtained through the index feature perceptron, and the indexes \(M_{1}^{\prime},\ldots,M_{n}^{\prime}\) are obtained by normalizing, filtering, and addressing \(M_{1},\ldots,M_{n}\). According to \(M_{1}^{\prime},\ldots,M_{n}^{\prime}\), partial template clouds \(T_{1}^{\prime},\ldots,T_{n}^{\prime}\) corresponding to \(S_{1},\ldots,S_{n}\) respectively are cut out from \(T\);
4. Partial template clouds and partial point clouds are input into the TMPE-Net in the form of correspondence point groups \(\big{\{}\big{(}S_{1},T_{1}^{\prime}\big{)},(S_{2},T_{2}^{\prime}),\ldots,(S_{n },T_{n}^{\prime}\big{)}\big{\}}\). In the TMPE -Net, the global feature vector \(\big{\{}\big{(}\varnothing(S_{1}),\varnothing(T_{1}^{\prime})\big{)},( \varnothing(S_{2}),\varnothing(T_{2}^{\prime})\big{)},\ldots,(\varnothing(S_{n }),\varnothing(T_{n}^{\prime})\big{)}\big{\}}\) of \(\big{\{}\big{(}S_{1},T_{1}^{\prime}\big{)},(S_{2},T_{2}^{\prime}),\ldots,(S_{n },T_{n}^{\prime})\big{\}}\) is obtained from the point cloud feature perceptron and the pooling layer, and each group of global feature vectors is spliced to obtain the global fusion feature \(F_{1}^{\prime},\ldots,F_{n}^{\prime}\);
5.