Skip to main content

ORIGINAL RESEARCH article

Front. Earth Sci.
Sec. Geohazards and Georisks
Volume 13 - 2025 | doi: 10.3389/feart.2025.1497871
This article is part of the Research Topic Physical Properties and Mechanical Theory of Rock Materials with Defects View all 9 articles

Improvement of rock surface roughness accuracy by combining object space resolution error and 3D point cloud features

Provisionally accepted
Jiang Yuan Jiang Yuan 1Qing Wang Qing Wang 1Qinzheng Yang Qinzheng Yang 2*Yongqiang Fan Yongqiang Fan 1Weining Jiao Weining Jiao 1
  • 1 CCCC Second Highway Engineering CO., Ltd., Xi'an, China
  • 2 School of Highway, Chang’an University, Xi'an, China

The final, formatted version of the article will be published soon.

    To enhance the accuracy of joint roughness coefficient (JRC) estimation in photogrammetry, this study employed a fixed-camera shooting strategy guided by a Structure-from-Motion-based shooting parameter selection algorithm to reconstruct 3D models of rock samples at 16 different shooting distances. The analysis at profile intervals of 0.25 mm, 0.5 mm, and 1 mm revealed a strong correlation between JRC accuracy and three parameters: object space resolution error, spatial distance between point cloud points, and spatial errors of checkpoints on the orientation board. Using these three parameters as input variables and JRC error as the output variable, five machine learning algorithms-Support Vector Regression, Gaussian Process Regression, Multilayer Perceptron, XGBoost, and CatBoost-were employed to predict JRC errors across different shooting distances. The Multilayer Perceptron model performed best at profile intervals of 0.25mm and 0.5mm, while XGBoost was optimal at the 1mm interval. Under the predictions of these models, JRC accuracy improved by an average of 84.7% across the three intervals. Finally, the applicability and limitations of the proposed method were further discussed.

    Keywords: Photogrammetry, rock surface roughness, JRC optimization, 3D reconstrution, machine learning

    Received: 18 Sep 2024; Accepted: 10 Jan 2025.

    Copyright: © 2025 Yuan, Wang, Yang, Fan and Jiao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Qinzheng Yang, School of Highway, Chang’an University, Xi'an, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.