NAVI: Category-Agnostic Image Collections with High-Quality 3D Shape and
  Pose Annotations
Authors: name: Varun Jampani, name: Kevis-Kokitsi Maninis, name: Andreas Engelhardt, name: Arjun Karpur, name: Karen Truong, name: Kyle Sargent, name: Stefan Popov, name: André Araujo, name: Ricardo Martin-Brualla, name: Kaushal Patel, name: Daniel Vlasic, name: Vittorio Ferrari, name: Ameesh Makadia, name: Ce Liu, name: Yuanzhen Li, name: Howard Zhou

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