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.

在过去的几年中，随着点云在低成本3D传感器上的广泛应用得到迅速发展，基于3D点云的对象识别变得越来越重要。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，这对识别任务具有负面影响。为了解决这个问题，提出了用于3D噪声点云的完整对象识别框架，其中首次集成了过滤的预处理步骤。在过滤阶段，我们考虑了两种拟议的方法，即制导3D点云滤波器（G3DF）和迭代制导法向滤波器（IGNF），以生成高质量的点云模型。然后，基于基于本地和基于全局的描述符的优势，一种新型的特征描述符，提出了一种称为局部到全局直方图（LGH）的方法，其中包含局部视点特征直方图（LVFH）和形状函数的局部集合（LESF）。实验结果表明，与其他现有技术组合相比，使用建议的过滤器和描述符产生的综合分类性能具有竞争力。特别是，G3DF和LVFH的组成更适合于实时应用。