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Camera pose estimation is a key step in standard 3D reconstruction pipelinesthat 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 availablebecause they rely on the ability to robustly identify and match visual featuresbetween image pairs. While these methods can work robustly with dense cameraviews, capturing a large set of images can be time-consuming or impractical. Wepropose SparsePose for recovering accurate camera poses given a sparse set ofwide-baseline images (fewer than 10). The method learns to regress initialcamera poses and then iteratively refine them after training on a large-scaledataset of objects (Co3D: Common Objects in 3D). SparsePose significantlyoutperforms conventional and learning-based baselines in recovering accuratecamera rotations and translations. We also demonstrate our pipeline forhigh-fidelity 3D reconstruction using only 5-9 images of an object.

SparsePose: Sparse-View Camera Pose Regression and Refinement