Deep neural network for incongruent point clouds registration

Sergei Voronin; Alexander Vasilyev; Vitaly Kober; Artyom Makovetskii; Aleksei Voronin; Dmitrii Zhernov

doi:10.1117/12.2677782

4 October 2023 Deep neural network for incongruent point clouds registration

Sergei Voronin, Alexander Vasilyev, Vitaly Kober, Artyom Makovetskii, Aleksei Voronin, Dmitrii Zhernov

Proceedings Volume 12674, Applications of Digital Image Processing XLVI; 126741L (2023) https://doi.org/10.1117/12.2677782
Event: SPIE Optical Engineering + Applications, 2023, San Diego, California, United States

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Abstract

Recently, there has been essential progress in the field of deep learning, which has led to compelling advances in most of the semantic tasks of computer vision, such as classification, detection, and segmentation. Point cloud registration is a task that aligns two or more different point clouds by evaluating the relative transformation between them. The Iterative Closest Points (ICP) algorithm and its variants have relatively good computational efficiency but are known to be subject to local minima, so rely on the quality of the initialization. In this paper, we propose a neural network based on the Deep Closest Points (DCP) neural network to solve the point cloud registration problem for incongruent point clouds. Computer simulation results are provided to illustrate the performance of the proposed method.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Sergei Voronin, Alexander Vasilyev, Vitaly Kober, Artyom Makovetskii, Aleksei Voronin, and Dmitrii Zhernov "Deep neural network for incongruent point clouds registration", Proc. SPIE 12674, Applications of Digital Image Processing XLVI, 126741L (4 October 2023); https://doi.org/10.1117/12.2677782

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