A simple neural network algorithm to register  3D point clouds

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

doi:10.1117/12.3027872

30 September 2024 A simple neural network algorithm to register 3D point clouds

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

Proceedings Volume 13137, Applications of Digital Image Processing XLVII; 131371E (2024) https://doi.org/10.1117/12.3027872
Event: Optical Engineering + Applications, 2024, San Diego, California, United States

Conference Poster

Abstract

3D point cloud registration is of great importance in robotics and computer vision to find a rigid body transformation to align a pair of point clouds with unknown point correspondences. In recent years, the deep learning model has dominated the field of computer vision. The important part of registration is the estimation of correspondences between point clouds. The main idea of studying correspondences between point clouds is to establish correspondences through the multidimensional features of each point. In this paper, we propose a simple neural network algorithm to register incongruent point clouds. The proposed algorithm utilizes the virtual points and is partially based on the PointNet++ neural network. Computer simulation results are provided to illustrate the performance of the proposed method.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Sergei Voronin, Vitaly Kober, Artyom Makovetskii, Aleksei Voronin, and Dmitrii Zhernov "A simple neural network algorithm to register 3D point clouds", Proc. SPIE 13137, Applications of Digital Image Processing XLVII, 131371E (30 September 2024); https://doi.org/10.1117/12.3027872

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