Towards an Automatic System for Extracting Planar Orientations from Software Generated Point Clouds

TL;DR

This paper presents GeoStructure, a machine learning system that automates the extraction of geological planar orientations from point clouds generated by Structure from Motion, improving efficiency and accuracy over manual methods.

Contribution

The paper introduces a novel methodology and software system for automatically measuring geological surface orientations directly from point clouds, bypassing traditional image-based techniques.

Findings

Effective noise mitigation using Mahalanobis distance

Accurate orientation measurements from reconstructed point clouds

Automated process reduces manual effort and improves consistency

Abstract

In geology, a key activity is the characterisation of geological structures (surface formation topology and rock units) using Planar Orientation measurements such as Strike, Dip and Dip Direction. In general these measurements are collected manually using basic equipment; usually a compass/clinometer and a backboard, recorded on a map by hand. Various computing techniques and technologies, such as Lidar, have been utilised in order to automate this process and update the collection paradigm for these types of measurements. Techniques such as Structure from Motion (SfM) reconstruct of scenes and objects by generating a point cloud from input images, with detailed reconstruction possible on the decimetre scale. SfM-type techniques provide advantages in areas of cost and usability in more varied environmental conditions, while sacrificing the extreme levels of data fidelity. Here is presented a methodology of data acquisition and a Machine Learning-based software system: GeoStructure, developed to automate the measurement of orientation measurements. Rather than deriving measurements using a method applied to the input images, such as the Hough Transform, this method takes measurements directly from the reconstructed point cloud surfaces. Point cloud noise is mitigated using a Mahalanobis distance implementation. Significant structure is characterised using a k-nearest neighbour region growing algorithm, and final surface orientations are quantified using the plane, and normal direction cosines.