  We present Deep Global Registration, a differentiable framework for pairwise
registration of real-world 3D scans. Deep global registration is based on three
modules: a 6-dimensional convolutional network for correspondence confidence
prediction, a differentiable Weighted Procrustes algorithm for closed-form pose
estimation, and a robust gradient-based SE(3) optimizer for pose refinement.
Experiments demonstrate that our approach outperforms state-of-the-art methods,
both learning-based and classical, on real-world data.
