Extraction of block walls from point clouds measured by Mobile Mapping System

To solve the problem of collapsing block walls widely used in Japan, this study proposes a method for extracting block walls using 3D point cloud data measured by the Mobile Mapping System (MMS). Unlike conventional methods, this method identifies block walls based on geometric features without relying on MMS trajectory data or deep learning inference results. In addition, the computational load is low and manual correction can be minimized. In our experiments, we used point cloud data collected in urban areas in Japan and achieved a precision of 0.750, recall of 0.810, and F-measure of 0.779. The results demonstrate the effectiveness of this method for automatic extraction of block walls and rapid assessment of collapse risk and are expected to contribute to safety measures in areas with high seismic risk

POINTNET++ TRANSFER LEARNING FOR TREE EXTRACTION FROM MOBILE LIDAR POINT CLOUDS

Trees are an essential part of the natural and urban environment due to providing crucial benefits such as increasing air quality and wildlife habitats. Therefore, various remote sensing and photogrammetry technologies, including Mobile Laser Scanner (MLS), have been recently introduced for precise 3D tree mapping and modeling. The MLS provides densely 3D LiDAR point clouds from the surrounding, which results in measuring applicable information of trees like stem diameter or elevation. In this paper, a transfer learning procedure on the PointNet++ has been proposed for tree extraction. Initially, two steps of converting the MLS point clouds into same-length smaller sections and eliminating ground points have been conducted to overcome the massive volume of MLS data. The algorithm was tested on four LiDAR datasets ranging from challengeable urban environments containing multiple objects like tall buildings to railway surroundings. F1-Score accuracy was gained at around 93% and 98%, which showed the feasibility and efficiency of the proposed algorithm. Noticeably, the algorithms also measured geometrical information of extracted trees such as 2D coordinate space, height, stem diameter, and 3D boundary tree locations

Extraction and Simplification of Building Façade Pieces from Mobile Laser Scanner Point Clouds for 3D Street View Services

Extraction and analysis of building façades are key processes in the three-dimensional (3D) building reconstruction and realistic geometrical modeling of the urban environment, which includes many applications, such as smart city management, autonomous navigation through the urban environment, fly-through rendering, 3D street view, virtual tourism, urban mission planning, etc. This paper proposes a building facade pieces extraction and simplification algorithm based on morphological filtering with point clouds obtained by a mobile laser scanner (MLS). First, this study presents a point cloud projection algorithm with high-accuracy orientation parameters from the position and orientation system (POS) of MLS that can convert large volumes of point cloud data to a raster image. Second, this study proposes a feature extraction approach based on morphological filtering with point cloud projection that can obtain building facade features in an image space. Third, this study designs an inverse transformation of point cloud projection to convert building facade features from an image space to a 3D space. A building facade feature with restricted facade plane detection algorithm is implemented to reconstruct façade pieces for street view service. The results of building facade extraction experiments with large volumes of point cloud from MLS show that the proposed approach is suitable for various types of building facade extraction. The geometric accuracy of building façades is 0.66 m in x direction, 0.64 in y direction and 0.55 m in the vertical direction, which is the same level as the space resolution (0.5 m) of the point cloud

Extraction and Simplification of Building Façade Pieces from Mobile Laser Scanner Point Clouds for 3D Street View Services

Extraction and analysis of building façades are key processes in the three-dimensional (3D) building reconstruction and realistic geometrical modeling of the urban environment, which includes many applications, such as smart city management, autonomous navigation through the urban environment, fly-through rendering, 3D street view, virtual tourism, urban mission planning, etc. This paper proposes a building facade pieces extraction and simplification algorithm based on morphological filtering with point clouds obtained by a mobile laser scanner (MLS). First, this study presents a point cloud projection algorithm with high-accuracy orientation parameters from the position and orientation system (POS) of MLS that can convert large volumes of point cloud data to a raster image. Second, this study proposes a feature extraction approach based on morphological filtering with point cloud projection that can obtain building facade features in an image space. Third, this study designs an inverse transformation of point cloud projection to convert building facade features from an image space to a 3D space. A building facade feature with restricted facade plane detection algorithm is implemented to reconstruct façade pieces for street view service. The results of building facade extraction experiments with large volumes of point cloud from MLS show that the proposed approach is suitable for various types of building facade extraction. The geometric accuracy of building façades is 0.66 m in x direction, 0.64 in y direction and 0.55 m in the vertical direction, which is the same level as the space resolution (0.5 m) of the point cloud