14/06/2020

Deep Global Registration

Christopher Choy, Wei Dong, Vladlen Koltun

Keywords: global registration, 3d features, 3d reconstruction, sparse tensor, convolutional network, inlier detection, deep learning

Abstract: 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.

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