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Novel methods for noisy 3D point cloud based object recognition

机译：基于嘈杂的3D点云对象识别的新方法

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摘要

3D point cloud based object recognition becomes increasingly important in the last few years, as the widely use of point cloud over the low-cost 3D sensors have developed rapidly. However, the obtained 3D point cloud is inevitably contaminated with noise due to physical and environmental factors, which has a negative impact on recognition task. To address this problem, a complete object recognition framework for 3D noisy point cloud is presented into which a pre-processing step of filtering is integrated for the first time. In the filtering phase, our two proposed approaches, named Guided 3D Point Cloud Filter (G3DF) and Iterative Guidance Normal Filter (IGNF), are taken into account to produce high-quality point cloud model. Then, on the basis of advantages of local-based and global-based descriptors, a new type of feature descriptor, called Local-to-Global Histogram (LGH), is proposed, which contains Local Viewpoint Feature Histogram (LVFH) and Local Ensemble of Shape Function (LESF). Experimental results show that the comprehensive classification performance yielded by using proposed filters and descriptors is competitive compared to other state-of-the-art combinations. In particularly, the composition of G3DF and LVFH is more suited for real-time applications.
机译：3D点云基于对象识别在过去几年中变得越来越重要，因为广泛使用的点云在低成本3D传感器上迅速发展。然而，由于物理和环境因素，所获得的3D点云不可避免地被噪声污染，这对识别任务产生了负面影响。为了解决这个问题，提出了一个完整的对象识别框架，用于3D噪声点云的识别框架，首次集成过滤的预处理步骤。在过滤阶段，考虑到我们两个提出的方法，命名的3D点云滤波器（G3DF）和迭代指南正常滤波器（IGNF）以产生高质量点云模型。然后，在基于本地基于和全局的描述符的优点的基础上，提出了一种新型的特征描述符，称为本地到全局直方图（LGH），其中包含本地视图特征直方图（LVFH）和本地集合形状功能（LESF）。实验结果表明，与其他最先进的组合相比，使用所提出的过滤器和描述符产生的综合分类性能与其他最先进的组合相比具有竞争力。特别地，G3DF和LVFH的组成更适合实时应用。

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