title: Doppelgangers: Learning to Disambiguate Images of Similar Structures
summary: We consider the visual disambiguation task of determining whether a pair of
  visually similar images depict the same or distinct 3D surfaces (e.g., the same
  or opposite sides of a symmetric building). Illusory image matches, where two
  images observe distinct but visually similar 3D surfaces, can be challenging
  for humans to differentiate, and can also lead 3D reconstruction algorithms to
  produce erroneous results. We propose a learning-based approach to visual
  disambiguation, formulating it as a binary classification task on image pairs.
  To that end, we introduce a new dataset for this problem, Doppelgangers, which
  includes image pairs of similar structures with ground truth labels. We also
  design a network architecture that takes the spatial distribution of local
  keypoints and matches as input, allowing for better reasoning about both local
  and global cues. Our evaluation shows that our method can distinguish illusory
  matches in difficult cases, and can be integrated into SfM pipelines to produce
  correct, disambiguated 3D reconstructions. See our project page for our code,
  datasets, and more results: http://doppelgangers-3d.github.io/.
paper_authors._id: 64f836239433a36edf4f482c
paper_authors.name: Ruojin Cai
paper_authors._id: 64f836239433a36edf4f482d
paper_authors.name: Joseph Tung
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-01-10T08:41:21.483Z
paper_authors._id: 656d6c26996819a828cbb55f
paper_authors.avatarUrl: /avatars/cbc80b438201752cc1ca45edc422bc50.svg
paper_authors.fullname: Joseph Tung
paper_authors.type: user
paper_authors.user: jotjot
paper_authors._id: 64f836239433a36edf4f482e
paper_authors.name: Qianqian Wang
paper_authors._id: 64f836239433a36edf4f482f
paper_authors.name: Hadar Averbuch-Elor
paper_authors._id: 64f836239433a36edf4f4830
paper_authors.name: Bharath Hariharan
paper_authors._id: 64f836239433a36edf4f4831
paper_authors.name: Noah Snavely
paper_title: Doppelgangers: Learning to Disambiguate Images of Similar Structures
paper_summary: We consider the visual disambiguation task of determining whether a pair of
  visually similar images depict the same or distinct 3D surfaces (e.g., the same
  or opposite sides of a symmetric building). Illusory image matches, where two
  images observe distinct but visually similar 3D surfaces, can be challenging
  for humans to differentiate, and can also lead 3D reconstruction algorithms to
  produce erroneous results. We propose a learning-based approach to visual
  disambiguation, formulating it as a binary classification task on image pairs.
  To that end, we introduce a new dataset for this problem, Doppelgangers, which
  includes image pairs of similar structures with ground truth labels. We also
  design a network architecture that takes the spatial distribution of local
  keypoints and matches as input, allowing for better reasoning about both local
  and global cues. Our evaluation shows that our method can distinguish illusory
  matches in difficult cases, and can be integrated into SfM pipelines to produce
  correct, disambiguated 3D reconstructions. See our project page for our code,
  datasets, and more results: http://doppelgangers-3d.github.io/.
paper_ai_summary: A learning-based approach using a custom dataset and network architecture distinguishes illusory image matches, improving 3D reconstruction accuracy.
paper_ai_keywords: binary classification image pairs Doppelgangers dataset spatial distribution local keypoints global cues SfM pipelines