Multiview Point Cloud Registration via Optimization in an Autoencoder
  Latent Space
Point cloud rigid registration is a fundamental problem in 3D computer
vision. In the multiview case, we aim to find a set of 6D poses to align a set
of objects. Methods based on pairwise registration rely on a subsequent
synchronization algorithm, which makes them poorly scalable with the number of
views. Generative approaches overcome this limitation, but are based on
Gaussian Mixture Models and use an Expectation-Maximization algorithm. Hence,
they are not well suited to handle large transformations. Moreover, most
existing methods cannot handle high levels of degradations. In this paper, we
introduce POLAR (POint cloud LAtent Registration), a multiview registration
method able to efficiently deal with a large number of views, while being
robust to a high level of degradations and large initial angles. To achieve
this, we transpose the registration problem into the latent space of a
pretrained autoencoder, design a loss taking degradations into account, and
develop an efficient multistart optimization strategy. Our proposed method
significantly outperforms state-of-the-art approaches on synthetic and real
data. POLAR is available at github.com/pypolar/polar or as a standalone package
which can be installed with pip install polaregistration.
6822175ee436561326740a3a
Luc Vedrenne
claimed_verified
2025-05-12T18:34:49.671Z
6300a2bc5ea4c617b216ef52
/avatars/ca26855881f8fba44fe9d8a2a353b8b0.svg
Luc Vedrenne
user
almotasim
6822175ee436561326740a3b
Sylvain Faisan
6822175ee436561326740a3c
Denis Fortun
Multiview Point Cloud Registration via Optimization in an Autoencoder
  Latent Space
Point cloud rigid registration is a fundamental problem in 3D computer
vision. In the multiview case, we aim to find a set of 6D poses to align a set
of objects. Methods based on pairwise registration rely on a subsequent
synchronization algorithm, which makes them poorly scalable with the number of
views. Generative approaches overcome this limitation, but are based on
Gaussian Mixture Models and use an Expectation-Maximization algorithm. Hence,
they are not well suited to handle large transformations. Moreover, most
existing methods cannot handle high levels of degradations. In this paper, we
introduce POLAR (POint cloud LAtent Registration), a multiview registration
method able to efficiently deal with a large number of views, while being
robust to a high level of degradations and large initial angles. To achieve
this, we transpose the registration problem into the latent space of a
pretrained autoencoder, design a loss taking degradations into account, and
develop an efficient multistart optimization strategy. Our proposed method
significantly outperforms state-of-the-art approaches on synthetic and real
data. POLAR is available at github.com/pypolar/polar or as a standalone package
which can be installed with pip install polaregistration.
POLAR, a method for multiview point cloud registration, excels in handling large numbers of views, significant degradations, and large initial angles by leveraging a latent space and specialized loss function.
point cloud registration multiview registration Gaussian Mixture Models Expectation-Maximization algorithm latent space pretrained autoencoder loss function optimization strategy