Learning to Estimate Multi-view Pose from Object Silhouettes.

While Structure-from-Motion pipelines certainly have their success cases in the task of 3D object reconstruction from multiple images, they still fail on many common objects that lack distinctive texture or have complex appearance qualities. The central problem lies in 6DOF camera pose estimation for the source images: without the ability to obtain a good estimate of the epipolar geometries, all state-of-the-art methods will fail. Although alternative solutions exist for specific objects, general solutions have proved elusive. In this work, we revisit the notion that silhouette cues can provide reasonable constraints on multi-view pose configurations when texture and priors are unavailable. Specifically, we train a neural network to holistically predict camera poses and pose confidences for a given set of input silhouette images, with the hypothesis that the network will be able to learn cues for multi-view relationships in a data-driven way. We show that our network generalizes to unseen synthetic and real object instances under reasonable assumptions about the input pose distribution of the images, and that the estimates are suitable to initialize state-of-the-art 3D reconstruction methods.