Automated texture mapping CityJSON 3D city models from oblique and nadir aerial imagery

The incorporation of detailed textures in 3D city models is crucial for enhancing their realism, as it adds depth and authenticity to the visual representation, thereby closely mimicking the surfaces and materials found in actual urban environments. Existing 3D city models can be enriched with energy-related roof and façade details, such as the material type (such as windows, green façades, bricks) and sunlight reflectance which can be derived from texture information. However, a common limitation of these models is their lack of very high resolution textures, which which reduces their realism and detail. Manually mapping textures onto each surface of a building is an exceptionally time-consuming and labor-intensive process, making it unfeasible for large-scale applications involving thousands of buildings. Therefore, an automated method is essential for texture mapping of 3D city models from aerial imagery. In this paper, we present CityJSON texture mapper – a python-based software tool for automated texture mapping of CityJSON-based 3D city models from oblique and nadir aerial imagery. Experimental results demonstrate the effectiveness of our approach in generating high-quality textured 3D city models, showcasing the potential for broader applications in geospatial analysis and decision-making. This research contributes to the ongoing efforts in enhancing the realism and usability of CityJSON-based 3D city models by enhancing them with their real textures from oblique aerial imagery. Texture mapped model can be explored at https://bit.ly/textured3dbag

Space Subdivision of Indoor Mobile Laser Scanning Data Based on the Scanner Trajectory

State-of-the-art indoor mobile laser scanners are now lightweight and portable enough to be carried by humans. They allow the user to map challenging environments such as multi-story buildings and staircases while continuously walking through the building. The trajectory of the laser scanner is usually discarded in the analysis, although it gives insight about indoor spaces and the topological relations between them. In this research, the trajectory is used in conjunction with the point cloud to subdivide the indoor space into stories, staircases, doorways, and rooms. Analyzing the scanner trajectory as a standalone dataset is used to identify the staircases and to separate the stories. Also, the doors that are traversed by the operator during the scanning are identified by processing only the interesting spots of the point cloud with the help of the trajectory. Semantic information like different space labels is assigned to the trajectory based on the detected doors. Finally, the point cloud is semantically enriched by transferring the labels from the annotated trajectory to the full point cloud. Four real-world datasets with a total of seven stories are used to evaluate the proposed methods. The evaluation items are the total number of correctly detected rooms, doors, and staircases

Semantic Interpretation of Mobile Laser Scanner Point Clouds in Indoor Scenes Using Trajectories

The data acquisition with Indoor Mobile Laser Scanners (IMLS) is quick, low-cost and accurate for indoor 3D modeling. Besides a point cloud, an IMLS also provides the trajectory of the mobile scanner. We analyze this trajectory jointly with the point cloud to support the labeling of noisy, highly reflected and cluttered points in indoor scenes. An adjacency-graph-based method is presented for detecting and labeling of permanent structures, such as walls, floors, ceilings, and stairs. Through occlusion reasoning and the use of the trajectory as a set of scanner positions, gaps are discriminated from real openings in the data. Furthermore, a voxel-based method is applied for labeling of navigable space and separating them from obstacles. The results show that 80% of the doors and 85% of the rooms are correctly detected, and most of the walls and openings are reconstructed. The experimental outcomes indicate that the trajectory of MLS systems plays an essential role in the understanding of indoor scene

VEHICLE RECOGNITION IN AERIAL LIDAR POINT CLOUD BASED ON DYNAMIC TIME WARPING

A two-step vehicle recognition method from an aerial Lidar point cloud is proposed in this paper. First, the Lidar point cloud is segmented using the region-growing algorithm with vehicle size limitation. Then the vehicle is recognized according to the profile shape based on dynamic time warping. The proposed method can detect vehicles parking under trees in an urban scene, and classifies the vehicles into different classes. The vehicle location, orientation, parking direction and size can also be determined. The experimental result based on a real urban Lidar point cloud shows that the proposed method can correctly recognize 95.1 % of vehicles