- Start Cloud Compare.
The CloudCompare application window appears. - Select File | Open.
The Open file(s) dialog box appears. - Browse and select a LAS lidar point cloud (*.las, *.laz) file e.g. lub_tile1.las. Click Open.
The LAS file point cloud is displayed. In this example, the blue points are ground points while the red points are the vegetation points. - With the mouse, click and drag anywhere within the graphic view. Roll the mouse wheel forward or backward to zoom in/put the graphic view.
A RGB 3-D direction indicator sphere appears and the point cloud display is reoriented according to the direction of the mouse movement. - In the DB Tree pane, select the point cloud e.g. lub_tile1 - Cloud.
- Select Edit | Scalar Fields | Filter by Value.
The Filter by scalar value dialog box appears. - Since we want to display only the ground points, change the Min and Max values to 2.0 (i.e. the LAS classification for ground). Click OK.
A point cloud lub_tile1 - Cloud.extract is extracted from the selected point cloud. - In the DB Tree, select the extracted point cloud. In the Properties pane, it may be necessary to change the display to LAS Classification instead of Return number.
- Select Edit | Mesh | Delaunay 2D (axis aligned plane).
A mesh is created from the ground points. However, it is hard to distinguish features from the display because there is no shadow or texture. - Select Display | Shaders & filters | E.D.L.
The EDL (Eye Dome Lighting) shader is applied. Now the ground texture can be seen
Monday, June 10, 2013
Display LiDAR LAS file in Cloud Compare
Cloud Compare, a 3-D point cloud and mesh processing open source software can be used to display LiDAR LAS files. Being a generic point cloud processing software, it has the tools to manipulate the point clouds including filtering by point scalar values e.g. LAS classification, editing, mesh creation, among other functions. The following steps demonstrate displaying a LAS file's ground points as a point cloud as well as a 3-D surface mesh.
Labels:
CloudCompare,
LiDAR,
point cloud
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