DeepCLR: Correspondence-Less Architecture for Deep End-to-End Point Cloud Registration

Horn M, Engel N, Belagiannis V, Buchholz M, Dietmayer K (2020)

Publication Type: Conference contribution

Publication year: 2020

Publisher: Institute of Electrical and Electronics Engineers Inc.

Conference Proceedings Title: 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020

Event location: Rhodes, GRC

ISBN: 9781728141497

DOI: 10.1109/ITSC45102.2020.9294279

Abstract

This work addresses the problem of point cloud registration using deep neural networks. We propose an approach to predict the alignment between two point clouds with overlapping data content, but displaced origins. Such point clouds originate, for example, from consecutive measurements of a LiDAR mounted on a moving platform. The main difficulty in deep registration of raw point clouds is the fusion of template and source point cloud. Our proposed architecture applies flow embedding to tackle this problem, which generates features that describe the motion of each template point. These features are then used to predict the alignment in an end-to-end fashion without extracting explicit point correspondences between both input clouds. We rely on the KITTI odometry and ModelNet40 datasets for evaluating our method on various point distributions. Our approach achieves state-of-the-art accuracy and the lowest run-time of the compared methods.

Authors with CRIS profile

Vasileios Belagiannis

Involved external institutions

Universität Ulm DE Germany (DE)

How to cite

APA:

Horn, M., Engel, N., Belagiannis, V., Buchholz, M., & Dietmayer, K. (2020). DeepCLR: Correspondence-Less Architecture for Deep End-to-End Point Cloud Registration. In 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020. Rhodes, GRC: Institute of Electrical and Electronics Engineers Inc..

MLA:

Horn, Markus, et al. "DeepCLR: Correspondence-Less Architecture for Deep End-to-End Point Cloud Registration." Proceedings of the 23rd IEEE International Conference on Intelligent Transportation Systems, ITSC 2020, Rhodes, GRC Institute of Electrical and Electronics Engineers Inc., 2020.

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