title: NAVI: Category-Agnostic Image Collections with High-Quality 3D Shape and
    Pose Annotations
summary: Recent advances in neural reconstruction enable high-quality 3D object
  reconstruction from casually captured image collections. Current techniques
  mostly analyze their progress on relatively simple image collections where
  Structure-from-Motion (SfM) techniques can provide ground-truth (GT) camera
  poses. We note that SfM techniques tend to fail on in-the-wild image
  collections such as image search results with varying backgrounds and
  illuminations. To enable systematic research progress on 3D reconstruction from
  casual image captures, we propose NAVI: a new dataset of category-agnostic
  image collections of objects with high-quality 3D scans along with per-image
  2D-3D alignments providing near-perfect GT camera parameters. These 2D-3D
  alignments allow us to extract accurate derivative annotations such as dense
  pixel correspondences, depth and segmentation maps. We demonstrate the use of
  NAVI image collections on different problem settings and show that NAVI enables
  more thorough evaluations that were not possible with existing datasets. We
  believe NAVI is beneficial for systematic research progress on 3D
  reconstruction and correspondence estimation. Project page:
  https://navidataset.github.io
paper_authors._id: 648c044f335645aa03f51f55
paper_authors.name: Varun Jampani
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-16T17:15:21.983Z
paper_authors._id: 630e7cfe3fc17ffc50f6e835
paper_authors.avatarUrl: /avatars/0b742ff094a09f9374fafcd97ab9e002.svg
paper_authors.fullname: Varun Jampani
paper_authors.type: user
paper_authors.user: varunjampani
paper_authors._id: 648c044f335645aa03f51f56
paper_authors.name: Kevis-Kokitsi Maninis
paper_authors._id: 648c044f335645aa03f51f57
paper_authors.name: Andreas Engelhardt
paper_authors._id: 648c044f335645aa03f51f58
paper_authors.name: Arjun Karpur
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-05-22T20:14:18.264Z
paper_authors._id: 664e2c8300dd56264275bb21
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/UlqJ-gpXxk1pu43TF89DS.jpeg
paper_authors.fullname: Arjun Karpur
paper_authors.type: user
paper_authors.user: arjunkarpur
paper_authors._id: 648c044f335645aa03f51f59
paper_authors.name: Karen Truong
paper_authors._id: 648c044f335645aa03f51f5a
paper_authors.name: Kyle Sargent
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-16T17:17:08.825Z
paper_authors._id: 6451a5d7938967fd069fd135
paper_authors.avatarUrl: /avatars/56a00b2e6211f678ec040ea379e7387a.svg
paper_authors.fullname: Kyle Sargent
paper_authors.type: user
paper_authors.user: ksahug
paper_authors._id: 648c044f335645aa03f51f5b
paper_authors.name: Stefan Popov
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-16T17:17:29.443Z
paper_authors._id: 63188baf57a5f5d7ce206423
paper_authors.avatarUrl: /avatars/3b1d8959207e2f518d80dbcaba7c0d96.svg
paper_authors.fullname: Stefan Popov
paper_authors.type: user
paper_authors.user: stefangp
paper_authors._id: 648c044f335645aa03f51f5c
paper_authors.name: André Araujo
paper_authors._id: 648c044f335645aa03f51f5d
paper_authors.name: Ricardo Martin-Brualla
paper_authors._id: 648c044f335645aa03f51f5e
paper_authors.name: Kaushal Patel
paper_authors._id: 648c044f335645aa03f51f5f
paper_authors.name: Daniel Vlasic
paper_authors._id: 648c044f335645aa03f51f60
paper_authors.name: Vittorio Ferrari
paper_authors._id: 648c044f335645aa03f51f61
paper_authors.name: Ameesh Makadia
paper_authors._id: 648c044f335645aa03f51f62
paper_authors.name: Ce Liu
paper_authors._id: 648c044f335645aa03f51f63
paper_authors.name: Yuanzhen Li
paper_authors._id: 648c044f335645aa03f51f64
paper_authors.name: Howard Zhou
paper_title: NAVI: Category-Agnostic Image Collections with High-Quality 3D Shape and
    Pose Annotations
paper_summary: Recent advances in neural reconstruction enable high-quality 3D object
  reconstruction from casually captured image collections. Current techniques
  mostly analyze their progress on relatively simple image collections where
  Structure-from-Motion (SfM) techniques can provide ground-truth (GT) camera
  poses. We note that SfM techniques tend to fail on in-the-wild image
  collections such as image search results with varying backgrounds and
  illuminations. To enable systematic research progress on 3D reconstruction from
  casual image captures, we propose NAVI: a new dataset of category-agnostic
  image collections of objects with high-quality 3D scans along with per-image
  2D-3D alignments providing near-perfect GT camera parameters. These 2D-3D
  alignments allow us to extract accurate derivative annotations such as dense
  pixel correspondences, depth and segmentation maps. We demonstrate the use of
  NAVI image collections on different problem settings and show that NAVI enables
  more thorough evaluations that were not possible with existing datasets. We
  believe NAVI is beneficial for systematic research progress on 3D
  reconstruction and correspondence estimation. Project page:
  https://navidataset.github.io
paper_ai_keywords: Structure-from-Motion (SfM) ground-truth (GT) camera poses high-quality 3D object reconstruction category-agnostic image collections dense pixel correspondences segmentation maps ground-truth camera parameters 2D-3D Alignments