Mohammad‐Mahdi Sharif

University of Waterloo

Papers

1

Total Citations

59

H-Index

1

About

Mohammad‐Mahdi Sharif is a leading researcher in the application of computer vision and machine learning to civil and construction engineering. His work focuses on automating the analysis of complex 3D point cloud data, a critical challenge for modern infrastructure management. Sharif’s most notable contribution is the development of automated, model-based methods for detecting and locating 3D objects within highly cluttered construction point cloud models. This foundational work, published in 2017 and garnering 59 citations, directly addresses the difficulty of extracting meaningful information from raw, unstructured spatial data. By enabling the automatic identification of building components in messy, real-world scans, his research bridges the gap between as-designed building information models (BIM) and as-built reality. This capability is essential for progress monitoring, quality control, and facility management. Sharif’s impact is evident in the adoption of his techniques for enhancing the efficiency and accuracy of construction inspection, reducing reliance on manual, time-consuming processes. His contributions are paving the way for more intelligent, data-driven approaches to managing the built environment, making him a key figure in the digital transformation of the construction industry.

Research Focus

Key Achievements

1
H-Index
1
Papers
59
Total Citations
59
Avg Citations/Paper
🏆 Most Cited Paper
Automated Model‐Based Finding of 3D Objects in Cluttered Construction Point Cloud Models
59 citations · 2017
📈 Most Prolific Year: 2017 (1 Papers)
🤝 Key Collaborators: 3
🏛 Institutions: University of Waterloo

Top Papers

  1. 1

Key Collaborators

Contact & Links

Available for collaboration
Content generated · 11 days ago