Neural network for 3D point clouds alignment

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

doi:10.1117/12.2633565

3 October 2022 Neural network for 3D point clouds alignment

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

Proceedings Volume 12226, Applications of Digital Image Processing XLV; 122261H (2022) https://doi.org/10.1117/12.2633565
Event: SPIE Optical Engineering + Applications, 2022, San Diego, California, United States

Conference Poster

Abstract

Point cloud is an important type of geometric data structure. Various applications require high-level point cloud processing. Instead of defining geometric elements such as corners and edges, state-of-the-art algorithms use semantic matching. These methods require learning-based approaches that rely on a statistical analysis of labeled datasets. Adapting deep learning techniques to handle 3D point clouds remains challenging. The standard deep neural network model requires regular inputs such as vectors and matrices. Three-dimensional point clouds are fundamentally irregular; that is, the positions of points are continuously distributed in space, and any permutation of their order does not change the spatial distribution. Modern deep neural networks are designed specifically to process point clouds directly, without going to an intermediate regular representation. The Deep Closest Point (DCP) network is a neural network that implements the ICP algorithm. DCP utilizes the point-to-point functional for error metric minimization. In this paper, we propose the modified variant of DCP based on other types of ICP error minimization functionals. Computer simulation results are provided to illustrate the performance of the proposed algorithm.

Citation Download Citation

Sergei Voronin, Alexander Vasilyev, Vitaly Kober, Artyom Makovetskii, Aleksei Voronin, and Dmitrii Zhernov "Neural network for 3D point clouds alignment", Proc. SPIE 12226, Applications of Digital Image Processing XLV, 122261H (3 October 2022); https://doi.org/10.1117/12.2633565

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