Consistent normal calculation of a point cloud

Is there a library in python or c++ that is capable of estimating normals of point clouds in a consistent way? In a consistent way I mean that the orientation of the normals is globally preserved over the surface.

For example, when I use python open3d package:

downpcd.estimate_normals(search_param=o3d.geometry.KDTreeSearchParamHybrid(
    radius=4, max_nn=300))

I get an inconsistent results, where some of the normals point inside while the rest point outside.

many thanks

If you know the viewpoint from where each point was captured, it can be used to orient the normals. I assume that this not the case - so given your situation, which seems rather watertight and uniformly sampled, mesh reconstruction can help.

PCL library offers the ConcaveHull algorithm. Although simple, it should be enough to produce a single coherent mesh.

Once you have a mesh, each triangle defines a normal (the cross product). It is important to note that a mesh isn't just a collection of independent faces. The faces are connected and this connectivity enforces a coherent orientation across the mesh.

pcl::PolygonMesh is an "half edge data structure". This means that every triangle face is defined by an ordered set of vertices, which defines the orientation: order of vertices => order of cross product => well defined unambiguous normals

You can either use the normals from the mesh (nearest neighbor), or calculate a low resolution mesh and just use it to orient the cloud.