# When Deep Learning Meets Data Alignment:
## 4 Review of Learning-Based Approaches for Registration
### Feature and Matching Level  
Learning approaches have demonstrated successful results in performing feature extraction and matching for registration purposes. Auto-encoders have been used for feature extraction. For instance, [PERSON] et al. [PERSON] _et al._ (2018) used in their proposal for point cloud registration a Deep Auto Encoder (DAE) for extracting low-dimensional descriptors from large scale point clouds. The training of the DAE is unsupervised, and it is able to extract a compact representation from depth maps that capture the significant geometric properties of the input data. [PERSON] et al. [PERSON] _et al._ (2018) proposed the method DeepGMR to perform the registration by matching points to a probability distribution whose parameters are estimated by a neural network from the input point clouds. That network learns latent correspondences between points and Gaussian Mixture Model (GMM) components that are pose-invariant. The network estimates point-to-component correspondences, following two compute blocks to obtain the GMM parameters and the transformation. [PERSON] et al. [PERSON] _et al._ (2018) introduced Shape Deformation Networks (SDNs) in an encoder-decoder architecture for matching deformable shapes, where the encoder is able to extract a global shape descriptor from a 3D model, while the decoder can transform the extracted descriptor into another model. The SDN is able to learn to deform a template shape to be aligned to targets with the articulated restriction. Concretely, the encoder SDN learns the deformation parameters and degrees of freedom to deform the template. This work shows that an encoder-decoder architecture to generate human shape correspondences can compete with state-of-the-art methods. Convolutional Neuronal Networks have also been used for feature extraction. [PERSON] et al. [PERSON] _et al._ (2018) propose ALIGNNet, an unsupervised network to align either 2D or 3D shapes with an incomplete target. The network learns to extract the features to match both shapes and compute Free Form Deformation (FFD) grids. It is trained with a shape alignment loss by comparing the overlap between the source and the target for learning the FFD parameters. [PERSON] et al. [PERSON] _et al._ (2018) developed a learning-based method to register multi-spectral images (visible and Near-Infra-Red images). They employed a learning approach for extracting features of both images and matching them. For that purpose, their proposal is based on an asymmetric (different weights) Siamese Convolutional Neural Network, one for each spectral channel. The networks minimize the Euclidean distance between the two descriptors. With a similar network architecture, [PERSON] and [PERSON] [PERSON] and [PERSON] (2018) proposed 3 DFet-Net, which finds features and descriptors as well as correspondences for later registration. They used coarsely annotated point clouds with GPS/INS absolute pose. It is based on a three-branch Siamese architecture that uses PointNet++ [PERSON] _et al._ (2018). Each branch takes an entire point cloud as input. The network is trained with a set of triplets containing the anchor and positive and negative point clouds. Positive point clouds are those with a distance to the anchor below a threshold, and negative point clouds are far away from the anchor. Each branch has a detector and descriptor network. Both networks for each branch share the same inputs. The detector network predicts an orientation and an attention weight for each branch. Then, the descriptor network rotates the input to a canonical configuration and computes the features that will be aligned with the other branch through a triplet loss. That loss aims to minimize the difference between the anchor and positive point cloud and maximize the difference between the anchor and the negative point cloud. To address the problem of inaccurate correspondences, [PERSON] et al.