Automated equipment recognition and classification from scattered point clouds for construction management applications

J. Chen, Y. Fang, Y. K. Cho

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

1 Citation (Scopus)


Recognizing construction assets from as-is point cloud data of construction environment provides essential information for engineering and management applications including progress monitoring, safety management, supply-chain management, and quality control. This study proposes a fast and automated processing pipeline for construction target assets recognition from scattered as-is point clouds. The recognition tasks can be subdivided into object detection, which involves computing the bounding box around each construction equipment, and object classification, which involves labelling point cloud clusters from discrete equipment categories, such as backhoe loader, bulldozer, dump truck, excavator and front loader. The object detection step consists of point cloud down-sampling, segmentation and clustering. For the object classification step, machine learning methods were employed to determine class membership probability using features derived from the ESF (Ensemble of Shape Functions) descriptor. The classifiers were trained on synthetic point clouds generated from CAD (Computer-aided Design) models. The method was validated using laser scanned point clouds from an equipment yard. The test results demonstrate promising advancements towards semantic labelling and scene understanding of point cloud data.

Original languageEnglish
Title of host publicationISARC 2016 - 33rd International Symposium on Automation and Robotics in Construction
PublisherInternational Association for Automation and Robotics in Construction (IAARC)
Number of pages8
Publication statusPublished - 2016
Externally publishedYes
EventInternational Symposium on Automation and Robotics in Construction 2016 - Auburn University, Auburn, United States of America
Duration: 18 Jul 201621 Jul 2016
Conference number: 33rd


ConferenceInternational Symposium on Automation and Robotics in Construction 2016
Abbreviated titleISARC 2016
CountryUnited States of America
Internet address


  • Laser scanning
  • Machine learning
  • Object classification
  • Object recognition
  • Scattered point clouds

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