Scene Coordinate Reconstruction: Posing of Image Collections via
  Incremental Learning of a Relocalizer
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/
66272795212635de6b91d638
Eric Brachmann
66272795212635de6b91d639
Jamie Wynn
66272795212635de6b91d63a
Shuai Chen
66272795212635de6b91d63b
Tommaso Cavallari
66272795212635de6b91d63c
Áron Monszpart
admin_assigned
2024-04-23T09:14:06.931Z
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Áron Monszpart
user
amonszpart
66272795212635de6b91d63d
Daniyar Turmukhambetov
66272795212635de6b91d63e
Victor Adrian Prisacariu
Scene Coordinate Reconstruction: Posing of Image Collections via
  Incremental Learning of a Relocalizer
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/
A learning-based approach, ACE0, estimates camera parameters from unposed images using scene coordinate regression, rivaling feature-based SfM in accuracy.
feature-based structure-from-motion incremental reconstruction triangulation registration visual relocalizer scene coordinate regression neural scene representations novel view synthesis