  Camera pose estimation is a key step in standard 3D reconstruction pipelines
that operate on a dense set of images of a single object or scene. However,
methods for pose estimation often fail when only a few images are available
because they rely on the ability to robustly identify and match visual features
between image pairs. While these methods can work robustly with dense camera
views, capturing a large set of images can be time-consuming or impractical. We
propose SparsePose for recovering accurate camera poses given a sparse set of
wide-baseline images (fewer than 10). The method learns to regress initial
camera poses and then iteratively refine them after training on a large-scale
dataset of objects (Co3D: Common Objects in 3D). SparsePose significantly
outperforms conventional and learning-based baselines in recovering accurate
camera rotations and translations. We also demonstrate our pipeline for
high-fidelity 3D reconstruction using only 5-9 images of an object.
