NAVI is a category-agnostic multi-view image collection dataset with high-quality 3D scans and precise 2D–3D alignments (per-image camera poses). It was created to enable systematic evaluation of image-based 3D reconstruction, multi-view geometric correspondence, and surface-level/keypoint correspondence tasks from casual (in-the-wild) image collections where traditional SfM often fails. NAVI provides object-centric image collections paired with near-perfect ground-truth camera parameters and 3D shapes, enabling extraction of accurate cross-view correspondences and evaluation following protocols such as Probe3D. Primary resources: NAVI project site (https://navidataset.github.io/), NeurIPS 2023 paper and supplemental materials, and the google/navi GitHub repository.
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