title: Scene Coordinate Reconstruction: Posing of Image Collections via
    Incremental Learning of a Relocalizer
summary: We address the task of estimating camera parameters from a set of images
  depicting a scene. Popular feature-based structure-from-motion (SfM) tools
  solve this task by incremental reconstruction: they repeat triangulation of
  sparse 3D points and registration of more camera views to the sparse point
  cloud. We re-interpret incremental structure-from-motion as an iterated
  application and refinement of a visual relocalizer, that is, of a method that
  registers new views to the current state of the reconstruction. This
  perspective allows us to investigate alternative visual relocalizers that are
  not rooted in local feature matching. We show that scene coordinate regression,
  a learning-based relocalization approach, allows us to build implicit, neural
  scene representations from unposed images. Different from other learning-based
  reconstruction methods, we do not require pose priors nor sequential inputs,
  and we optimize efficiently over thousands of images. Our method, ACE0 (ACE
  Zero), estimates camera poses to an accuracy comparable to feature-based SfM,
  as demonstrated by novel view synthesis. Project page:
  https://nianticlabs.github.io/acezero/
paper_authors._id: 66272795212635de6b91d638
paper_authors.name: Eric Brachmann
paper_authors._id: 66272795212635de6b91d639
paper_authors.name: Jamie Wynn
paper_authors._id: 66272795212635de6b91d63a
paper_authors.name: Shuai Chen
paper_authors._id: 66272795212635de6b91d63b
paper_authors.name: Tommaso Cavallari
paper_authors._id: 66272795212635de6b91d63c
paper_authors.name: Áron Monszpart
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-04-23T09:14:06.931Z
paper_authors._id: 64c8bf0cc864d962edf975aa
paper_authors.avatarUrl: /avatars/25357e9413b37f1e3058916215062c2a.svg
paper_authors.fullname: Áron Monszpart
paper_authors.type: user
paper_authors.user: amonszpart
paper_authors._id: 66272795212635de6b91d63d
paper_authors.name: Daniyar Turmukhambetov
paper_authors._id: 66272795212635de6b91d63e
paper_authors.name: Victor Adrian Prisacariu
paper_title: Scene Coordinate Reconstruction: Posing of Image Collections via
    Incremental Learning of a Relocalizer
paper_summary: We address the task of estimating camera parameters from a set of images
  depicting a scene. Popular feature-based structure-from-motion (SfM) tools
  solve this task by incremental reconstruction: they repeat triangulation of
  sparse 3D points and registration of more camera views to the sparse point
  cloud. We re-interpret incremental structure-from-motion as an iterated
  application and refinement of a visual relocalizer, that is, of a method that
  registers new views to the current state of the reconstruction. This
  perspective allows us to investigate alternative visual relocalizers that are
  not rooted in local feature matching. We show that scene coordinate regression,
  a learning-based relocalization approach, allows us to build implicit, neural
  scene representations from unposed images. Different from other learning-based
  reconstruction methods, we do not require pose priors nor sequential inputs,
  and we optimize efficiently over thousands of images. Our method, ACE0 (ACE
  Zero), estimates camera poses to an accuracy comparable to feature-based SfM,
  as demonstrated by novel view synthesis. Project page:
  https://nianticlabs.github.io/acezero/
paper_ai_keywords: structure-from-motion (SfM) triangulation sparse 3D points registration point cloud visual relocalizer scene coordinate regression neural scene representations unposed images pose priors novel view synthesis