Deep Global Registration

• URL: http://arxiv.org/abs/2004.11540v2
• Date: Fri, 8 May 2020 08:59:52 GMT
• Title: Deep Global Registration
• Authors: Christopher Choy, Wei Dong, Vladlen Koltun
• Abstract summary: Deep Global Registration is a differentiable framework for pairwise registration of real-world 3D scans. Our approach outperforms state-of-the-art methods, both learning-based and classical, on real-world data.
• Score: 90.05565444450524
• License: http://creativecommons.org/licenses/by/4.0/
• 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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