When Deep Learning Meets Data Alignment: A Review on Deep Registration Networks (DRNs)

Abstract

Registration is the process that computes the transformation that aligns sets of data. Commonly, a registration process can be divided into four main steps: target selection, feature extraction, feature matching, and transform computation for the alignment. The accuracy of the result depends on multiple factors, the most significant are the quantity of input data, the presence of noise, outliers and occlusions, the quality of the extracted features, real-time requirements and the type of transformation, especially those ones defined by multiple parameters, like non-rigid deformations. Recent advancements in machine learning could be a turning point in these issues, particularly with the development of deep learning (DL) techniques, which are helping to improve multiple computer vision problems through an abstract understanding of the input data. In this paper, a review of deep learning-based registration methods is presented. We classify the different papers proposing a framework extracted from the traditional registration pipeline to analyse the new learning-based proposal strengths. Deep Registration Networks (DRNs) try to solve the alignment task either replacing part of the traditional pipeline with a network or fully solving the registration problem. The main conclusions extracted are, on the one hand, 1) learning-based registration techniques cannot always be clearly classified in the traditional pipeline. 2) These approaches allow more complex inputs like conceptual models as well as the traditional 3D datasets. 3) In spite of the generality of learning, the current proposals are still ad hoc solutions. Finally, 4) this is a young topic that still requires a large effort to reach general solutions able to cope with the problems that affect traditional approaches.

arXiv commentPublished in Applied Sciences

References in corpus (22)

• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
• A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation
• PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
• Learning from Synthetic Humans
• An Unsupervised Learning Model for Deformable Medical Image Registration
• The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches
• 3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration
• A Review on Deep Learning Techniques for Video Prediction
• Sim-to-Real Reinforcement Learning for Deformable Object Manipulation
• Non-rigid image registration using fully convolutional networks with deep self-supervision
• A Visually Plausible Grasping System for Object Manipulation and Interaction in Virtual Reality Environments
• Unsupervised End-to-end Learning for Deformable Medical Image Registration
• Deep Learning for 3D Point Cloud Understanding: A Survey
• 3D non-rigid registration using color: Color Coherent Point Drift
• Deform-GAN:An Unsupervised Learning Model for Deformable Registration
• Coherent Point Drift Networks: Unsupervised Learning of Non-Rigid Point Set Registration
• DeepGMR: Learning Latent Gaussian Mixture Models for Registration
• Unsupervised 3D Reconstruction from a Single Image via Adversarial Learning
• 3D-PhysNet: Learning the Intuitive Physics of Non-Rigid Object Deformations
• Deep Multi-Spectral Registration Using Invariant Descriptor Learning
• High-level Reasoning and Low-level Learning for Grasping: A Probabilistic Logic Pipeline
• Learning Manifold Patch-Based Representations of Man-Made Shapes

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• Vision-based Robotic Grasping From Object Localization, Object Pose Estimation to Grasp Estimation for Parallel Grippers: A Review
• A Review on Deep Learning Techniques for Video Prediction
• Rigid Transformations for Stabilized Lower Dimensional Space to Support Subsurface Uncertainty Quantification and Interpretation
• Global Point Cloud Registration Network for Large Transformations