# When Deep Learning Meets Data Alignment:
## 5 Conclusions  
In this work, approaches in the intersection between registration and deep neural networks are reviewed. It is important to remark that a large part of the works was reached through the ArXiv repository, which at the moment, even though is not peer-reviewed, leads the advancements in many fields such as artificial intelligence and deep learning. For this reason, extra effort was required to \begin{table}
\begin{tabular}{l} \hline \hline _target selection_ \\ \hline \hline  * Reconstruction of 3D models from a single 2D image using encoder–decoder architectures [18; 68] * Leverage partial 3D observations to generate complete 3D models (mesh completion) using Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) for predicting missing geometry [39; 50] * The use of Adversarial Training (AT) to predict structural deformations on 3D meshes given a multi-modal input (e.g., forces, material properties, visual imaging) [61] \\ \hline \hline \end{tabular}
\begin{tabular}{l} \hline \hline _features and matching_ \\ \hline \hline  * Registration of global and local point clouds with a generic Deep Auto Encoder (DAE) architecture regardless of the input data and its source [15] * The Shape Deformation Network (SDN) is an autoencoder architecture able to deal with deformable shapes extracting global shape descriptors [29] * Capability for multi-spectral registration using Asymmetric Siamese Convolutional Networks [57] * Filtering inaccurate features correspondences based on geometrical and global properties to minimize their influence in the registration process [67; 73] * Registration process speedup using a two-staged approach based on Convolutional Neural Networks (CNNs) to solve coarse-to-fine registration problems [63] \\ \hline \hline _transform_ \\ \hline \hline  * Using Spatial Transformer Networks (STNs) to deal with geometrical variability by means of learned invariance to transformations [45; 47; 95] * To align multimodal inputs with transformations generated and evaluated in an adversarial fashion [60] * Ability to preserve detailed geometric information by using a CNN to infer Free Form Deformation (FFD) parameters for 3D template-image matching [65] * Unsupervised registration of multiple point clouds in a global frame of reference [27] * Outperforming single channel CNNs with a multi-channel approach for real-time deformable registration [64] * Cloth fitting by modeling 3D body-garment interaction in real time surpassing Physics-Based Simulation (PBS) [36] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of the key advantages of the reviewed methods. select the papers. However, this approach enables including the most recent works in the scope of this paper. Registration aims to calculate a common reference for two or more datasets. This field has been widely studied, but recent techniques in machine learning are being combined with registration algorithms to increase the capabilities of the proposals. These techniques include neural networks with many hidden layers, also known as deep learning, and its novelty remains in the ability to learn representations from huge amounts of data at different levels of abstraction. Applied to registration, this paradigm allows managing higher-level understanding problems that are more related to conceptual knowledge of the scene rather than the geometric properties. We name this paradigm Deep Registration Networks (DRNs) to identify the branch of artificial intelligence exploring solutions for alignment problems using DNNs. The contribution of this work is a review of registration methods based on deep networks. To do that, the learning approaches for registration are reviewed and classified using a novel framework extracted from the traditional registration pipeline. The review clearly identifies the current efforts and existing gaps in the intersection between registration and learning algorithms.