G3Point

Granulometry from 3D Point clouds

Based on Steer, Guerit et al. (2022)

Introduction

G3Point is a tool which aims at automatically measuring the size, shape, and orientation of a large number of individual grains as detected from any type of 3D point clouds describing the topography of surfaces covered by sediments. The tool has been developped initially in Matlab https://github.com/philippesteer/G3Point

This repository aims at converting the tool in Python at first and also to try to improve it it a longer term.

This algorithm relies on 3 main phases:

1. Grain segmentation using a waterhsed algorithm
2. Grain merging and cleaning
3. Grain fitting by geometrical models including ellipsoids and cuboids

HOWTO

First you have to import the g3point module.
Note: it's up to you to configure correctly the python path for your system to be able to find it.

import g3point

To instantiate a G3Point object, you will need a point cloud, in las or ply and an ini file (see the example in the data section).

g3point_data = g3point.G3Point(cloud, ini)

Initial segmentation

The initial segmentation is done once and for all, it is not modified by the clustering or by the cleaning. IT sets the following attributes:

• initial_labels
• initial_sink_indexes
• initial_stacks

At the end of the initial segmentation, the variables labels, sink_indexes and stacks are initialized to their initial_ counterparts.

g3point_data.initial_segmentation()

Cluster

The clustering is based on the initial segmentation.

g3point_data.cluster()

Clean

The cleaning is based on the current state of the labels

g3point_data.clean()

Save the results

out, out_sinks = g3point_data.save()

Ellipsoid fitting

Once you have a group of points, it is possible to fit an ellipsoid to this group. This is not done for the g3point_data object as a whole at the current time.

center, radii, quaternions, rotation_matrix, ellipsoid_parameters = g3point.fit_ellipsoid_to_grain(xyz)