CloudCompare forum

Hello, I'm new to CloudCompare software and I'm trying to convert a point cloud into a mesh with good accuracy. I'm also working on creating a workflow for this process. So far, I understand that I need to clean the point cloud, compute accurate normals, and then create the mesh.

I would like to ask: Is there a standard process I can follow? Additionally, are there any differences in how I should process point clouds from different environments? For example, I have one point cloud of a room and another of a street. Are there any specific steps I should take when processing these two different types?

Also, should I convert the entire point cloud into a single mesh, or is it better to split the cloud into different parts, like the ground, walls, etc., and convert them into separate meshes?

Hi,

I am not aware of any particular process (apart the high level one you describe).

But clearly, depending on the environment / cloud, computing normals might be more or less challenging (or I should say more or even more challenging ;). I would say that the only way to ensure some quality here is to use structured data (Faro / E57 / etc. with scan grids, or at least with individual scans and the sensor position).

Hi, I've seen the topic about scan grids and sensors discussed many times, especially regarding their effect on computing normals for point clouds. I know that sensors are shown in the point cloud as TLS/GBL in the 3D view. What exactly is a scan grid? Is it the yellow box? And what is the difference between using sensors versus using a scan grid?

The scan grids are only displayed in the cloud properties (as a list, as there can be multiple for a single cloud).

It's simply an array/table that gives the point acquired for each position of the scanner (generally organized as a regular grid because of the horizontal and vertical angular steps of the sensor).