With the wide application of Light Detection and Ranging (LiDAR) in the collection of high-precision environmental point cloud information, three-dimensional (3D) object classification from point clouds has become an important research topic. However, the characteristics of LiDAR point clouds, such as unstructured distribution, disordered arrangement, and large amounts of data, typically result in high computational complexity and make it very difficult to classify 3D objects. Thus, this paper proposes a Convolutional Neural Network (CNN)-based 3D object classification method using the Hough space of LiDAR point clouds to overcome these problems. First, object point clouds are transformed into Hough space using a Hough transform algorithm, and then the Hough space is rasterized into a series of uniformly sized grids. The accumulator count in each grid is then computed and input to a CNN model to classify 3D objects. In addition, a semi-automatic 3D object labeling tool is developed to build a LiDAR point clouds object labeling library for four types of objects (wall, bush, pedestrian, and tree). After initializing the CNN model, we apply a dataset from the above object labeling library to train the neural network model offline through a large number of iterations. Experimental results demonstrate that the proposed method achieves object classification accuracy of up to 93.3% on average.

随着光探测与测距（LiDAR）在高精度环境点云信息采集中的广泛应用，点云的三维（3D）物体分类已成为一个重要的研究课题。然而，LiDAR点云的非结构化分布、无序排列和数据量大等特点通常会导致计算复杂度较高，使得3D物体分类变得非常困难。因此，本文提出了一种基于卷积神经网络（CNN）的利用激光雷达点云霍夫空间的3D物体分类方法来克服这些问题。首先，使用霍夫变换算法将目标点云变换到霍夫空间，然后将霍夫空间光栅化为一系列大小均匀的网格。然后计算每个网格中的累加器计数并将其输入到 CNN 模型以对 3D 对象进行分类。此外，还开发了半自动3D对象标记工具，为四种类型的对象（墙壁、灌木、行人和树木）构建LiDAR点云对象标记库。初始化CNN模型后，我们应用上述对象标记库中的数据集，通过大量迭代离线训练神经网络模型。实验结果表明，该方法的目标分类准确率平均高达93.3%。