Demo: Build Terrain Mesh¶

This demo illustrates how to build a terrain mesh from a point cloud.

To run the demo, type:

$ python build_terrain_mesh.py

Purpose¶

This demo demonstrates how to download point cloud data for a specified area, process the data by removing global outliers, and build a terrain mesh from the processed point cloud. Finally, the resulting terrain mesh is visualized.

Step-by-step¶

1. Define Bounds: Specify the spatial bounds for a residential area in Helsingborg by setting a height/width value (here, 2000.0 units).

   h = 2000.0
   bounds = dtcc.Bounds(319891, 6399790, 319891 + h, 6399790 + h)
   

2. Download Point Cloud: Retrieve the point cloud data (lidar) for the defined bounds using the download_pointcloud function.

   pointcloud = dtcc.download_pointcloud(bounds=bounds)
   

3. Process Data: Remove global outliers from the point cloud to filter out noise and improve the quality of the mesh.

   pointcloud = pointcloud.remove_global_outliers(3.0)
   

4. Build Terrain Mesh: Construct a terrain mesh from the processed point cloud. The build_terrain_mesh function allows you to specify parameters such as maximum mesh size, minimum mesh angle, and smoothing factor.

   mesh = dtcc.build_terrain_mesh(
       pointcloud,
       max_mesh_size=10,
       min_mesh_angle=25,
       smoothing=3,
   )
   

5. Visualize the Mesh: View the generated terrain mesh using the view() method.

   mesh.view()
   

Complete Code¶

Below is the complete code for this demo:

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# This demo illustrates how to build a terrain mesh from a point cloud.

import dtcc

# Define bounds (a residential area in Helsingborg)
h = 2000.0
bounds = dtcc.Bounds(319891, 6399790, 319891 + h, 6399790 + h)

# Download point cloud
pointcloud = dtcc.download_pointcloud(bounds=bounds)

# Remove global outliers
pointcloud = pointcloud.remove_global_outliers(3.0)

# Build terrain mesh
mesh = dtcc.build_terrain_mesh(
    pointcloud,
    max_mesh_size=10,
    min_mesh_angle=25,
    smoothing=3,
)

# View terrain mesh
mesh.view()