GAFAR: Graph-Attention Feature-Augmentation for Registration A Fast and
  Light-weight Point Set Registration Algorithm
Ludwig Mohr Ismail Geles Friedrich Fraundorfer
Rigid registration of point clouds is a fundamental problem in computer
vision with many applications from 3D scene reconstruction to geometry capture
and robotics. If a suitable initial registration is available, conventional
methods like ICP and its many variants can provide adequate solutions. In
absence of a suitable initialization and in the presence of a high outlier rate
or in the case of small overlap though the task of rigid registration still
presents great challenges. The advent of deep learning in computer vision has
brought new drive to research on this topic, since it provides the possibility
to learn expressive feature-representations and provide one-shot estimates
instead of depending on time-consuming iterations of conventional robust
methods. Yet, the rotation and permutation invariant nature of point clouds
poses its own challenges to deep learning, resulting in loss of performance and
low generalization capability due to sensitivity to outliers and
characteristics of 3D scans not present during network training. In this work,
we present a novel fast and light-weight network architecture using the
attention mechanism to augment point descriptors at inference time to optimally
suit the registration task of the specific point clouds it is presented with.
Employing a fully-connected graph both within and between point clouds lets the
network reason about the importance and reliability of points for registration,
making our approach robust to outliers, low overlap and unseen data. We test
the performance of our registration algorithm on different registration and
generalization tasks and provide information on runtime and resource
consumption. The code and trained weights are available at
https://github.com/mordecaimalignatius/GAFAR/.