title: Multiview Point Cloud Registration via Optimization in an Autoencoder
    Latent Space
summary: 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.
paper_authors._id: 6822175ee436561326740a3a
paper_authors.name: Luc Vedrenne
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-12T18:34:49.671Z
paper_authors._id: 6300a2bc5ea4c617b216ef52
paper_authors.avatarUrl: /avatars/ca26855881f8fba44fe9d8a2a353b8b0.svg
paper_authors.fullname: Luc Vedrenne
paper_authors.type: user
paper_authors.user: almotasim
paper_authors._id: 6822175ee436561326740a3b
paper_authors.name: Sylvain Faisan
paper_authors._id: 6822175ee436561326740a3c
paper_authors.name: Denis Fortun
paper_title: Multiview Point Cloud Registration via Optimization in an Autoencoder
    Latent Space
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: point cloud registration multiview registration Gaussian Mixture Models Expectation-Maximization algorithm latent space pretrained autoencoder loss function optimization strategy