NAVI: Category-Agnostic Image Collections with High-Quality 3D Shape and
  Pose Annotations
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
648c044f335645aa03f51f55
Varun Jampani
admin_assigned
2023-06-16T17:15:21.983Z
630e7cfe3fc17ffc50f6e835
/avatars/0b742ff094a09f9374fafcd97ab9e002.svg
Varun Jampani
user
varunjampani
648c044f335645aa03f51f56
Kevis-Kokitsi Maninis
648c044f335645aa03f51f57
Andreas Engelhardt
648c044f335645aa03f51f58
Arjun Karpur
claimed_verified
2024-05-22T20:14:18.264Z
664e2c8300dd56264275bb21
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/UlqJ-gpXxk1pu43TF89DS.jpeg
Arjun Karpur
user
arjunkarpur
648c044f335645aa03f51f59
Karen Truong
648c044f335645aa03f51f5a
Kyle Sargent
admin_assigned
2023-06-16T17:17:08.825Z
6451a5d7938967fd069fd135
/avatars/56a00b2e6211f678ec040ea379e7387a.svg
Kyle Sargent
user
ksahug
648c044f335645aa03f51f5b
Stefan Popov
admin_assigned
2023-06-16T17:17:29.443Z
63188baf57a5f5d7ce206423
/avatars/3b1d8959207e2f518d80dbcaba7c0d96.svg
Stefan Popov
user
stefangp
648c044f335645aa03f51f5c
André Araujo
648c044f335645aa03f51f5d
Ricardo Martin-Brualla
648c044f335645aa03f51f5e
Kaushal Patel
648c044f335645aa03f51f5f
Daniel Vlasic
648c044f335645aa03f51f60
Vittorio Ferrari
648c044f335645aa03f51f61
Ameesh Makadia
648c044f335645aa03f51f62
Ce Liu
648c044f335645aa03f51f63
Yuanzhen Li
648c044f335645aa03f51f64
Howard Zhou
NAVI: Category-Agnostic Image Collections with High-Quality 3D Shape and
  Pose Annotations
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
NAVI, a new dataset with high-quality 3D scans and precise 2D-3D alignments, facilitates advanced evaluations for 3D reconstruction and correspondence estimation from casual image captures.
Structure-from-Motion (SfM) category-agnostic image collections 3D scans 2D-3D alignments camera parameters dense pixel correspondences depth maps segmentation maps