In the production of high-definition maps, it is necessary to achieve the three-dimensional instantiation of road furniture that is difficult to depict on traditional maps. The development of mobile laser measurement technology provides a new means for acquiring road furniture data. To address the issue of traffic marking extraction accuracy in practical production, which is affected by degradation, occlusion, and non-standard variations, this paper proposes a 3D reconstruction method based on energy functions and template matching, using zebra crossings in vehicle-mounted LiDAR point clouds as an example. First, regions of interest (RoIs) containing zebra crossings are obtained through manual selection. Candidate point sets are then obtained at fixed distances, and their neighborhood intensity features are calculated to determine the number of zebra stripes using non-maximum suppression. Next, the slice intensity feature of each zebra stripe is calculated, followed by outlier filtering to determine the optimized length. Finally, a matching template is selected, and an energy function composed of the average intensity of the point cloud within the template, the intensity information entropy, and the intensity gradient at the template boundary is constructed. The 3D reconstruction result is obtained by solving the energy function, performing mode statistics, and normalization. This method enables the complete 3D reconstruction of zebra stripes within the RoI, maintaining an average planar corner accuracy within 0.05 m and an elevation accuracy within 0.02 m. The matching and reconstruction time does not exceed 1 s, and it has been applied in practical production.

Point cloud semantic segmentation helps Intelligent Transportation Systems understand traffic scenes by assigning semantic label to each point in the point cloud, and it relies on large amounts of annotated training data. Nevertheless, manually annotating large-scale datasets of complex traffic scenes is quite time-consuming and tedious. This paper proposes INF-PCA, an interactive point cloud semantic annotation method based on implicit neural field, which allows users to achieve high-quality, large-scene and fast-response semantic annotation with only a few dozen mouse clicks. Firstly, the appearance, geometry and semantics of the point clouds are jointly represented by an implicit neural field, which maps a 3D spatial coordinate to its corresponding attributes. Secondly, an uncertainty-based semantic entropy loss and a supervoxel-based local consistency loss are designed to force the network to produce deterministic predictions with local consistency, thus generating smoother and more accurate boundaries. Furthermore, an active learning-based strategy for click-free annotation is proposed and analyzed to further reduce annotation pressure. Comprehensive experiments on multiple datasets including the road scene dataset Toronto3D revealed that INF-PCA can achieve more accurate annotations with faster response speed and only half of the clicks employed by the state-of-the-art methods, and that INF-PCA can be directly applied to intelligent transportation applications such as interactive segmentation of road scenes, inventory of transportation infrastructure assets, and production of high-definition map.

The point clouds collected by the airborne laser scanning (ALS) system provide accurate 3-D information of urban land covers. By utilizing multitemporal ALS point clouds, semantic changes in urban areas can be captured, demonstrating significant potential in urban planning, emergency management, and infrastructure maintenance. Existing 3-D change detection (CD) methods struggle to efficiently extract multiclass semantic information and change features, still facing the following challenges: 1) the difficulty of accurately modeling cross-temporal point clouds spatial relationships for effective change feature extraction; 2) class imbalance of change samples which hinders distinguishability of semantic features; and 3) the lack of real-world datasets for 3-D semantic CD. To resolve these challenges, we propose the multitask (MT) enhanced cross-temporal point transformer (ME-CPT) network. By establishing spatio-temporal correspondences across different epochs, ME-CPT utilizes attention mechanisms to extract semantic change features. In addition, we incorporate a semantic segmentation task leveraging an MT training strategy to further bolster the discriminative power of semantic features while mitigating the adverse effects of class imbalance in change types. Moreover, we release a 22.5 km2 3-D semantic CD dataset, offering diverse scenes for comprehensive evaluation. Experiments on multiple datasets show that the proposed ME-CPT achieves superior performance compared to existing methods. The source code and dataset will be released at https://github.com/WHU-USI3DV/ME-CPT