Mobile Light Detection And Ranging (LiDAR) point clouds have the characteristics of complex and incomplete scenes, uneven point density and noises, which raises great challenges for automatically interpreting 3D scene. Aiming at the problem of 3D point cloud classification, we propose a probabilistic graphical model for automatic classification of mobile LiDAR point clouds in this paper. First, the super-voxels are generated as primitives based on the similar geometric and radiometric properties. Second, we construct point-based multi-scale visual features that are used to describe the texture information at various scales. Third, the topic model is used to analyze the semantic correlations among points within super-voxels to establish the semantic representation, which is finally fed into the proposed probabilistic graphical model. The proposed model combines Bayesian network and Markov random fields to obtain locally continuous and globally optimal classification results. To evaluate the effectiveness and the robustness of the proposed method, experiments were conducted using mobile LiDAR point clouds for three types of street scenes. Experimental results demonstrate that our proposed model is efficient and robust for extracting vehicles, buildings, street trees and pole-like objects, with overall accuracies of 98.17%, 97.41% and 96.81% respectively. Moreover, compared with other existing methods, our proposed model can provide higher classification correctness, specifically for small objects such as cars and pole-like objects.

移动光检测与测距（LiDAR）点云具有场景复杂和不完整，点密度不均匀和噪声的特征，这给自动解释3D场景提出了巨大的挑战。针对3D点云分类问题，本文提出了一种用于移动LiDAR点云自动分类的概率图形模型。首先，基于相似的几何和辐射特性，将超级体素生成为图元。其次，我们构造基于点的多尺度视觉特征，这些特征用于描述各种尺度的纹理信息。第三，主题模型用于分析超体素内各点之间的语义相关性，以建立语义表示，最终将其输入到所提出的概率图形模型中。提出的模型结合了贝叶斯网络和马尔可夫随机场，以获得局部连续和全局最优的分类结果。为了评估该方法的有效性和鲁棒性，使用移动LiDAR点云对三种类型的街道场景进行了实验。实验结果表明，本文提出的模型提取车辆，建筑物，行道树和杆状物体的效率高，鲁棒性强，总体准确率分别为98.17％，97.41％和96.81％。此外，与其他现有方法相比，我们提出的模型可以提供更高的分类正确性，特别是对于小物体（例如汽车和类似杆的物体）。为了评估该方法的有效性和鲁棒性，使用移动LiDAR点云对三种类型的街道场景进行了实验。实验结果表明，本文提出的模型提取车辆，建筑物，街道树木和杆状物体的效率高，鲁棒性好，总体准确率分别为98.17％，97.41％和96.81％。此外，与其他现有方法相比，我们提出的模型可以提供更高的分类正确性，特别是对于小物体（例如汽车和类似杆的物体）。为了评估该方法的有效性和鲁棒性，使用移动LiDAR点云对三种类型的街道场景进行了实验。实验结果表明，本文提出的模型提取车辆，建筑物，行道树和杆状物体的效率高，鲁棒性强，总体准确率分别为98.17％，97.41％和96.81％。此外，与其他现有方法相比，我们提出的模型可以提供更高的分类正确性，特别是对于小物体（例如汽车和类似杆的物体）。街道树木和杆状物体，总体准确度分别为98.17％，97.41％和96.81％。此外，与其他现有方法相比，我们提出的模型可以提供更高的分类正确性，特别是对于小物体（例如汽车和类似杆的物体）。街道树木和杆状物体，总体准确度分别为98.17％，97.41％和96.81％。此外，与其他现有方法相比，我们提出的模型可以提供更高的分类正确性，特别是对于小物体（例如汽车和类似杆的物体）。