A learned detector method for point cloud registration

Liyin Zhang; Yi Yang; Zhenhua Xiong; Liu Chao

doi:10.1117/12.2589346

20 January 2021 A learned detector method for point cloud registration

Liyin Zhang, Yi Yang, Zhenhua Xiong, Liu Chao

Proceedings Volume 11719, Twelfth International Conference on Signal Processing Systems; 1171917 (2021) https://doi.org/10.1117/12.2589346
Event: Twelfth International Conference on Signal Processing Systems, 2020, Shanghai, China

Abstract

In this paper, we propose a Detector-Net method for point cloud registration which learns a 3D feature detector of a specific descriptor. Different from the traditional detectors, deep neural network is used to generate this detector and manual annotation of feature points is not required. Instead, we leverage the aligned point cloud to deduce distinguishing points to generate training data. The indoor point cloud dataset is used as the training set, and experimental results show that the Detector-Net has better accuracy among traditional detectors.

Citation Download Citation

Liyin Zhang, Yi Yang, Zhenhua Xiong, and Liu Chao "A learned detector method for point cloud registration", Proc. SPIE 11719, Twelfth International Conference on Signal Processing Systems, 1171917 (20 January 2021); https://doi.org/10.1117/12.2589346

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