ABSTRACT This paper proposes a framework for automatic classification of mobile laser scanner (MLS) point cloud using multi-faceted multi-object convolutional neural network (MMCN). The proposed method takes a full three-dimensional (3D) point cloud as input and outputs a class label for each point. Unlike other existing classification methods for MLS data, the proposed method is not dependent on any parameter or its tuning. The proposed MMCN uses multiple objects of a sample, defined by different sizes of the sample, in addition to the different facets obtained by rotating about the various axes, thus adding more information during the training and testing stages. The proposed framework uses manually extracted samples for training the MMCN. Automatically extracted multiple regions based on the various radii of spherical neighbourhoods around MLS points are passed through the trained MMCN for determining their probabilities of belonging to different classes. The class probabilities of different sized regions are then used as a feature vector to train a support vector machine (SVM), and the final decision for the class of a point is based on the SVM output. The proposed framework has been trained for five classes, viz., Ground, House, Pole, Tree, and Car and has been tested on Oakland and Paris-Lille 3D MLS datasets. The total accuracy and kappa coefficient (κ) reach up to 96.5% and 93.8%, respectively, for the framework. The MMCN together with the SVM is able to achieve parameter-free classification of MLS data, thereby eliminating the need for manual parameter tuning as in the existing methods. Therefore, besides the use for classification of MLS data for mapping purpose, the approach is also suitable for classification of light detection and ranging (LiDAR) data resulting from autonomous vehicle sensors. The accuracy of this work can be further improved by incorporating more and varied training samples and deeper convolutional neural network (CNN) with better hardware resources.

中文翻译：

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使用多区域和 3D CNN 架构的移动 LiDAR 数据自动分类框架

摘要 本文提出了一种使用多面多目标卷积神经网络 (MMCN) 自动分类移动激光扫描仪 (MLS) 点云的框架。所提出的方法将一个完整的三维（3D）点云作为输入，并为每个点输出一个类标签。与其他现有的 MLS 数据分类方法不同，所提出的方法不依赖于任何参数或其调整。所提出的 MMCN 使用一个样本的多个对象，由不同大小的样本定义，以及通过围绕各个轴旋转获得的不同面，从而在训练和测试阶段添加更多信息。提议的框架使用手动提取的样本来训练 MMCN。基于MLS点周围球形邻域的各种半径自动提取的多个区域通过经过训练的MMCN以确定它们属于不同类别的概率。然后将不同大小区域的类别概率用作特征向量来训练支持向量机（SVM），最终决定点的类别基于 SVM 输出。所提出的框架已针对五个类别进行了训练，即地面、房屋、杆子、树和汽车，并已在奥克兰和巴黎-里尔 3D MLS 数据集上进行了测试。该框架的总准确率和 kappa 系数 (κ) 分别达到 96.5% 和 93.8%。MMCN 与 SVM 一起能够实现 MLS 数据的无参数分类，从而消除了现有方法中手动参数调整的需要。因此，除了用于映射目的的 MLS 数据分类之外，该方法还适用于自动车辆传感器产生的光检测和测距 (LiDAR) 数据的分类。通过结合更多和不同的训练样本和具有更好硬件资源的更深层次的卷积神经网络 (CNN)，可以进一步提高这项工作的准确性。