The integration system of Building Information Modelling (BIM) and Light Detection and Ranging (LiDAR) help
- to come up with real-time onsite quality information collecting and processing for construction quality control;
- Capture the existing of cultural historical sites and historical building for preservation and maintenance because that paper-based drawings may not available or there is no drawings for the historical monuments;
- Capture existing condition for survey
Following is LiDAR to BIM process
(1)Terrestrial LiDAR Point Cloud Model
3D SCENCE REGISTRATION
3D registration is the process of consistently aligning two or more sets of three-dimensional points.
A point cloud includes a set of 3D points with known coordinates, in many applications, could be acquired by 3D scanners from different viewpoints and are expressed in the sensor frame. The coordinates indicate the positions of the points in the building environments without any semantic or high-level geometric as-built building information.
The registration is always necessary in the process of capturing, retrieving, and modeling as-built building information, since one scan typically cannot capture all as-built conditions in a building environment. Here, the global coordinate system is defined as the coordinate system used by the point cloud in the first scan. The point clouds in other scans are aligned to the cloud in the first scan. First, the visual feature detectors are used to determine the positions of the features in the color images. The features are distinctively described with feature descriptors. Then, the common features in consecutive color images. Based on the matching results, the pairs of 3D matching points can be determined by referring to the locations of the matched feature positions in the color images and their corresponding depth values. This way, the point clouds in the scans could be progressively registered.
The registration process leads to the position and orientation between views in the same coordinate frame, such that overlapping areas between the point clouds match as well as possible. Once aligned, the individual point clouds can be fused into a single one, so that subsequent processing steps such as object reconstruction and building inspection can be applied. Point cloud registration appears as a recurring task for a number of applications ranging from computer vision, computer graphics and robotic perception to photogrammetry, cultural heritage modeling and digital archeology
ALSO, the 3D points collected by laser scanners only record the spatial as-built building conditions, which limits the capability to recognize building elements. In order to retrieve this information, building elements in the environments need to be recognized from the point cloud. This way, the geometries and dimensions of the elements can be estimated and modeled correspondingly.
In the point cloud based methods, terrestrial laser scanners are commonly used to capture the detailed as-built building conditions. One laser scan may collect millions of 3D points in minutes. Using this rich information, proposed an idea of creating as-built building floor plans by projecting the collected 3D points onto a vertical (z-) axis and a ground (x-y) plane. The projection results could indicate which points can be grouped. This way, the floor plans were created.
Existing recognition methods mainly relied on the points spatial features, which can be described globally or locally. Global descriptors captured all the geometrical characteristics of a desired object. Therefore, they are discriminative but not robust to cluttered scenes. Local descriptors improved the recognition robustness, but they were computationally complex. Both global and local descriptors cannot recognize building elements with different materials. For example, it is difficult for the methods to differentiate between a concrete column and a wooden column just based on their 3D points, if both of them have the same shape and size.
HOWEVER, the direct recognition of the building elements from the point cloud has proven difficult, especially when the detailed prior information is not available
THEREFORE, the visual features of building elements has been exploited here. Under the workflow of building elements recognition, the elements are first recognized in the color images based on their unique visual patterns. The patterns include the topological configurations of the elements’ contour and texture features. For example, concrete columns in buildings are dominated by long near-vertical lines (contour features) at sides and concrete surfaces (texture feature) in the middle, no matter they are rectangular or circular. Therefore, they can be located by searching such cues in the color images. When building elements are recognized from the color images, the recognition results could be used to classify the 3D points, so that the points belonging to the same building elements could be grouped and modeled separately.
(2)The transformation process from point cloud model to BIM model.
-Process of Model Format Transformation
Point cloud model through Geomagic XOS software, from the mesh surface of the build program to convert into a solid three-dimensional model, the model outputs *.xrl model project file, in order to make *.xrl model project file can then be further converted into BIM models in part of the format still need additional processing.
In this study, which will *.xrl model project file from Geomagic XOS Software export to AutoCAD DXF format, for between AutoCAD and other types of software were data file format CAD data exchange, and also can be compatible with Autodesk Revit software format, there will be no data loss problem when switching between the two, in the follow-up BIM way to integrate, so *.xrl project file model can import to Revit, followed by the concept of BIM related applications.
-Process of the BIM Model Development
Through three-dimensional point cloud model produced by terrestrial laser scanners instrument scan output, after the format conversion processing, import reverse engineering software to build faceted manner become triangular mesh model, but so far all processes, outputs three-dimensional models are the object does not contain any information related to the model, only a three-dimensional model can be displayed visually, but through integration with BIM platform, the model import Autodesk Revit, which can set up this model a variety of information associated to new models, extending a three-dimensional model of the overall application efficiency.
Using Revit can add a variety of information in model, such as materials and functional properties of the model related to, these data with the model correspond to each other and are having connectivity. Because use of this manner to builds model cannot in Revit do material filling, therefore, need an additional procedure, is use Navisworks to do the material filling and rendering.