Robust-DefReg: A Robust Deformable Point Cloud Registration Method based
  on Graph Convolutional Neural Networks
Sara Monji-Azad Marvin Kinz Jürgen Hesser
Point cloud registration is a fundamental problem in computer vision that
aims to estimate the transformation between corresponding sets of points.
Non-rigid registration, in particular, involves addressing challenges including
various levels of deformation, noise, outliers, and data incompleteness. This
paper introduces Robust-DefReg, a robust non-rigid point cloud registration
method based on graph convolutional networks (GCNNs). Robust-DefReg is a
coarse-to-fine registration approach within an end-to-end pipeline, leveraging
the advantages of both coarse and fine methods. The method learns global
features to find correspondences between source and target point clouds, to
enable appropriate initial alignment, and subsequently fine registration. The
simultaneous achievement of high accuracy and robustness across all challenges
is reported less frequently in existing studies, making it a key objective of
the Robust-DefReg method. The proposed method achieves high accuracy in large
deformations while maintaining computational efficiency. This method possesses
three primary attributes: high accuracy, robustness to different challenges,
and computational efficiency. The experimental results show that the proposed
Robust-DefReg holds significant potential as a foundational architecture for
future investigations in non-rigid point cloud registration. The source code of
Robust-DefReg is available.