Although 3D point cloud classification neural network models have been widely used, the in-depth interpretation of the activation of the neurons and layers is still a challenge. We propose a novel approach, named Relevance Flow, to interpret the hidden semantics of 3D point cloud classification neural networks. It delivers the class Relevance to the activated neurons in the intermediate layers in a back-propagation manner, and associates the activation of neurons with the input points to visualize the hidden semantics of each layer. Specially, we reveal that the 3D point cloud classification neural network has learned the plane-level and part-level hidden semantics in the intermediate layers, and utilize the normal and IoU to evaluate the consistency of both levels' hidden semantics. Besides, by using the hidden semantics, we generate the adversarial attack samples to attack 3D point cloud classifiers. Experiments show that our proposed method reveals the hidden semantics of the 3D point cloud classification neural network on ModelNet40 and ShapeNet, which can be used for the unsupervised point cloud part segmentation without labels and attacking the 3D point cloud classifiers.

Mobile laser scanning (MLS) systems mainly comprise laser scanners and mobile mapping platforms. Typical MLS systems are able to acquire three-dimensional point clouds with 1-10 centimeter point spacing at a normal driving or walking speed in the street or indoor environments. The MLS' advantages of efficiency and stability make it a quite practical tool for three-dimensional urban modeling. This paper reviews the latest advances in 3D modeling of the LiDAR-based mobile mapping system (MMS) point cloud, including LiDAR Simultaneous Localization and Mapping (SLAM), point cloud registration, feature extraction, object extraction, semantic segmentation, and deep learning processing. Then typical urban modeling applications based on MMS are also discussed.