Compute Normals for Point Cloud with Open3D and Poisson Algorithm

compute normals for point cloud using open3d and poisson algorithm, and write to file

To compute normals for a point cloud using Open3D and the Poisson algorithm, and then write the results to a file, you can follow these steps. Below is a Python script that demonstrates this process:

1. Load the point cloud.
2. Estimate the normals.
3. Use the Poisson surface reconstruction to compute the normals.
4. Save the point cloud with normals to a file.

First, make sure you have Open3D installed. You can install it using pip if you haven't already:

pip install open3d

Now, here is the Python script:

import open3d as o3d

# Load the point cloud from a file
pcd = o3d.io.read_point_cloud("input.ply")  # Replace with your input file

# Estimate normals
pcd.estimate_normals(search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=0.1, max_nn=30))

# Perform Poisson surface reconstruction
with o3d.utility.VerbosityContextManager(o3d.utility.VerbosityLevel.Debug) as cm:
    mesh, densities = o3d.geometry.TriangleMesh.create_from_point_cloud_poisson(pcd, depth=9)

# Save the point cloud with normals to a file
o3d.io.write_triangle_mesh("output_with_normals.ply", mesh)

# Optionally, visualize the point cloud with normals
o3d.visualization.draw_geometries([mesh], point_show_normal=True)

Explanation:

1. Loading the Point Cloud:

   • The point cloud is loaded from a file using o3d.io.read_point_cloud("input.ply"). Replace "input.ply" with the path to your point cloud file.

2. Estimating Normals:

   • Normals are estimated using the estimate_normals method. The search_param parameter specifies the radius and the maximum number of nearest neighbors to use for the KDTree search.

3. Poisson Surface Reconstruction:

   • The Poisson surface reconstruction is performed using create_from_point_cloud_poisson. The depth parameter controls the depth of the octree used for the reconstruction. A higher depth value results in a more detailed mesh but requires more computation.

4. Saving the Point Cloud with Normals:

   • The resulting mesh, which includes the computed normals, is saved to a file using o3d.io.write_triangle_mesh("output_with_normals.ply", mesh). Replace "output_with_normals.ply" with your desired output file path.

5. Visualization (Optional):

   • The point cloud with normals can be visualized using o3d.visualization.draw_geometries.

Make sure to replace the file paths with your actual input and output file paths. This script assumes that the input file is in PLY format, but Open3D