一种点云配准的学习检测器方法

Liyin Zhang, Yi Yang, Z. Xiong, Liu Chao
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引用次数: 1

摘要

本文提出了一种点云配准的detector - net方法,该方法学习特定描述符的三维特征检测器。与传统的检测器不同,该检测器采用深度神经网络生成,不需要人工标注特征点。相反,我们利用对齐的点云来推断区分点来生成训练数据。以室内点云数据集作为训练集,实验结果表明,Detector-Net在传统检测器中具有更好的准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A learned detector method for point cloud registration
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.
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