Main defect of the structured light scanning is that the edge part is lost in the point clouds of scanned object. This
research tried to combine the image processing method to a structured light system in order to improve the quality of the
point cloud. The technique approaches are present, and the results are given as below: after overlying the edge part of
the 3D model to the original point cloud from the structured light system, their hiatus can be restored and the resolution
of the original point cloud can be improved.
Airborne LiDAR data have become cost-effective to produce at local and regional scales across the United States and
internationally. These data are typically collected and processed into surface data products by contractors for state and
local communities. Current algorithms for advanced processing of LiDAR point cloud data are normally implemented in
specialized, expensive software that is not available for many users, and these users are therefore unable to experiment
with the LiDAR point cloud data directly for extracting desired feature classes. The objective of this research is to
identify and assess automated, readily implementable GIS procedures to extract features like buildings, vegetated areas,
parking lots and roads from LiDAR data using standard image processing tools, as such tools are relatively mature with
many effective classification methods. The final procedure adopted employs four distinct stages. First, interpolation is
used to transfer the 3D points to a high-resolution raster. Raster grids of both height and intensity are generated. Second,
multiple raster maps - a normalized surface model (nDSM), difference of returns, slope, and the LiDAR intensity map -
are conflated to generate a multi-channel image. Third, a feature space of this image is created. Finally, supervised
classification on the feature space is implemented. The approach is demonstrated in both a conceptual model and on a
complex real-world case study, and its strengths and limitations are addressed.
The aim of this paper is to present new method that can be used for automatically extracting 3D models in the terrestrial
laser scanning (TLS) point clouds of Chinese traditional architecture. Based on the inherent geometric and topological
constraints in Chinese traditional architectures, spatial direction and topology analysis are used to express the rules. We
develop a rule-based automatic modelling algorithm and apply it to extract the wooden structural elements.
In ground based Lidar system, the targets are used in the process of registration, georeferencing for point cloud, and also
can be used as check points. Generally, the accuracy of capturing the flat target center is influenced by scanning range
and scanning angle. In this research, the experiments are designed to extract accuracy index of the target center with
0-90°scan angles and 100-195 meter scan ranges using a Leica HDS3000 laser scanner. The data of the experiments are
listed in detail and the related results are analyzed.
An application study of Visualization Toolkit (VTK) in three dimension terrain visualization is expatiated in this work.
The research scope contained matching of digital terrain model with individual building model for 3D terrain
visualization, in order to improve the geometric integration of them to optimize the displaying of 3DGIS for construction
objects. A tested example in the research was about Grid DEM matching with an individual building model, which had a
polygon base surface as constraint conditions to the Grid DEM, for enhancing the efficiency of 3D visualization.