An Improved Random Forest Model Applied to Point Cloud Classification

Dong, Xue; Cheng, Yinglei; Xiaosong, Shi; Yan, Fei; Wen, Pei

doi:10.1088/1757-899x/768/7/072037

Cited by 8 publications

(5 citation statements)

References 13 publications

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“…However, a major drawback of this method is that it can create complex trees due to small changes in data [45]. Random Forest (RF) is a method that creates a large collection of uncorrelated trees and then uses bootstrap aggregation (also known as bagging) to average them [46], which appears to improve accuracy and allows the model to score the estimation results leading to a winner-takes-all driven outcome [47]. This method is relatively simple to implement if compared to the other methods as it follows a similar logic to human thinking.…”

Section: Supervised Machine Learning Methodsmentioning

confidence: 99%

Machine and Deep Learning Implementations for Heritage Building Information Modelling: A Critical Review of Theoretical and Applied Research

Gil,

Arayici,

Kumar

et al. 2024

J. Comput. Cult. Herit.

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Research domain and Problem: HBIM modelling from point cloud data has become a crucial research topic in the last decade since it is potentially considered the central data model paving the way for the digital heritage practice beyond digitization. Reality Capture technologies such as terrestrial laser scanning, drone-mounted LiDAR sensors and photogrammetry enable the reality capture with a sub-millimetre accurate point cloud file that can be used as a reference file for Heritage Building Information Modelling (HBIM). However, HBIM modelling from the point cloud data of heritage buildings is mainly manual, error-prone, and time-consuming. Furthermore, image processing techniques are insufficient for classification and segmenting of point cloud data to speed up and enhance the current workflow for HBIM modelling. Due to the challenges and bottlenecks in the scan-to-HBIM process, which is commonly criticized as complex with its bespoke requirements, semantic segmentation of point clouds is gaining popularity in the literature. Research Aim and Methodology: Therefore, this paper aims to provide a thorough critical review of Machine Learning and Deep Learning methods for point cloud segmentation, classification, and BIM geometry automation for cultural heritage case study applications. Research findings: This paper files the challenges of HBIM practice and the opportunities for semantic point cloud segmentation found across academic literature in the last decade. Beyond definitions and basic occurrence statistics, this paper discusses the success rates and implementation challenges of machine and deep learning classification methods. Research value and contribution: This paper provides a holistic review of point cloud segmentation and its potential for further development and application in the Cultural Heritage sector. The critical analysis provides insight into the current state-of-the-art methods and advises on their suitability for HBIM projects. The review has identified highly original threads of research, which hold the potential to significantly influence practice and further applied research. HBIM, Point Cloud, Semantic Segmentation, Classification, Machine Learning, Deep Learning

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Section: Supervised Machine Learning Methodsmentioning

confidence: 99%

Machine and Deep Learning Implementations for Heritage Building Information Modelling: A Critical Review of Theoretical and Applied Research

Gil,

Arayici,

Kumar

et al. 2024

J. Comput. Cult. Herit.

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“…The extracted point cloud data were used to detect and classify weld defects at the level of their morphological characteristics. For this, a modified method was used with further analysis based on the random forest model [39][40][41]. We implemented and developed the procedure for the three-dimensional reconstruction of the surface of the pipeline weld.…”

Section: Methodsmentioning

confidence: 99%

Machine Learning and 3D Reconstruction of Materials Surface for Nondestructive Inspection

Kartashov

Чернов

Alexandrov

et al. 2022

Sensors

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During the steel pipeline installation, special attention is paid to the butt weld control performed by fusion welding. The operation of the currently popular automated X-ray and ultrasonic testing complexes is associated with high resource and monetary costs. In this regard, this work is devoted to the development of alternative and cost-effective means of preliminary quality control of the work performed based on the visual testing method. To achieve this goal, a hardware platform based on a single board Raspberry Pi4 minicomputer and a set of available modules and expansion cards is proposed, and software whose main functionality is implemented based on the systemic application of computer vision algorithms and machine learning methods. The YOLOv5 object detection algorithm and the random forest machine learning model were used as a defect detection and classification system. The mean average precision (mAP) of the trained YOLOv5 algorithm based on extracted weld contours is 86.9%. A copy of YOLOv5 trained on the images of control objects showed a mAP result of 96.8%. Random forest identifying of the defect precursor based on the point clouds of the weld surface achieved a mAP of 87.5%.

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“…Li et al [Li16] or Xue et al [Xue20] for a random forest classification algorithm. This way, objects such as trees or cars can be eliminated and only points corresponding to the ground approximated.…”

Section: Outliersmentioning

confidence: 99%

LR B-Spline Surfaces and Volumes for Deformation Analysis of Terrain Data

Kermarrec

Skytt

Dokken

2022

Optimal Surface Fitting of Point Clouds Using Local Refinement

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Geospatial data acquisition of terrains with contact-free sensors such as Terrestrial or Airbone Laser Scanners generates scattered and noisy point clouds. Performing a surface approximation is an efficient way to reduce and structure the recorded point clouds. To that end, LR B-splines are attractive as they allow a local refinement, on the contrary to the tensor product B-spline and raster surfaces. By comparing the approximation error with a given tolerance, a local refinement is performed. We apply this adaptive refinement strategy to landslides data sets from Alpine terrain in Austria. We show how different epochs of the point clouds can be analyzed with LR B-spline volumes for spatio-temporal visualisation of deformation. We highlight the potential of a time-differenced LR B-splines volume for analysing geomorphological changes. A further application of this method is the drawing of contour lines.

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An Improved Random Forest Model Applied to Point Cloud Classification

Cited by 8 publications

References 13 publications

Machine and Deep Learning Implementations for Heritage Building Information Modelling: A Critical Review of Theoretical and Applied Research

Machine and Deep Learning Implementations for Heritage Building Information Modelling: A Critical Review of Theoretical and Applied Research

Machine Learning and 3D Reconstruction of Materials Surface for Nondestructive Inspection

LR B-Spline Surfaces and Volumes for Deformation Analysis of Terrain Data

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