Accurate Point Cloud Registration with Robust Optimal Transport

• URL: http://arxiv.org/abs/2111.00648v1
• Date: Mon, 1 Nov 2021 01:34:46 GMT
• Title: Accurate Point Cloud Registration with Robust Optimal Transport
• Authors: Zhengyang Shen, Jean Feydy, Peirong Liu, Ariel Hern\'an Curiale, Ruben San Jose Estepar, Raul San Jose Estepar, Marc Niethammer
• Abstract summary: We show that recent OT solvers improve both optimization-based and deep learning methods for point cloud registration. Our work demonstrates that robust OT enables fast pre-alignment and fine-tuning for a wide range of registration models.
• Score: 16.386335031156005
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This work investigates the use of robust optimal transport (OT) for shape matching. Specifically, we show that recent OT solvers improve both optimization-based and deep learning methods for point cloud registration, boosting accuracy at an affordable computational cost. This manuscript starts with a practical overview of modern OT theory. We then provide solutions to the main difficulties in using this framework for shape matching. Finally, we showcase the performance of transport-enhanced registration models on a wide range of challenging tasks: rigid registration for partial shapes; scene flow estimation on the Kitti dataset; and nonparametric registration of lung vascular trees between inspiration and expiration. Our OT-based methods achieve state-of-the-art results on Kitti and for the challenging lung registration task, both in terms of accuracy and scalability. We also release PVT1010, a new public dataset of 1,010 pairs of lung vascular trees with densely sampled points. This dataset provides a challenging use case for point cloud registration algorithms with highly complex shapes and deformations. Our work demonstrates that robust OT enables fast pre-alignment and fine-tuning for a wide range of registration models, thereby providing a new key method for the computer vision toolbox. Our code and dataset are available online at: https://github.com/uncbiag/robot.

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