非一致点云配准的深度神经网络

S. Voronin, A. Vasilyev, V. Kober, A. Makovetskii, A. Voronin, Dmitrii Zhernov
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引用次数: 0

摘要

最近,深度学习领域取得了重大进展,在计算机视觉的大多数语义任务(如分类、检测和分割)方面取得了令人瞩目的进展。点云配准是一项通过计算两个或多个不同点云之间的相对变换来对齐点云的任务。迭代最近点(ICP)算法及其变体具有相对较好的计算效率,但已知会受到局部极小值的影响,因此依赖于初始化的质量。在本文中,我们提出了一种基于深度最近点(DCP)神经网络的神经网络来解决不一致点云的点云配准问题。计算机仿真结果验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep neural network for incongruent point clouds registration
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.
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