Dr Ali Hosseininaveh, K. N. Toosi University of Technology, Iran
Dr. Ali Hosseininaveh is an Assistant Professor at K.N. Toosi University of Technology, Iran, specializing in photogrammetry and geomatics engineering. He holds a PhD from University College London (2014), an MSc from K.N. Toosi University, and a BSc from Islamic Azad University. He has led the Department of Photogrammetry and Remote Sensing since 2018 and previously managed the Close Range Photogrammetry Lab. Dr. Hosseininaveh teaches advanced courses in photogrammetry, computer vision, and robotics at both MSc and PhD levels. His research includes innovative methods for visual-inertial SLAM, unmanned aerial vehicle orthophoto generation, and 3D modeling. 📐🌍📸🧑🏫
Publication Profile
google scholar
Education
Dr. Ali Hosseininaveh is an accomplished academic with a strong background in photogrammetry and surveying. He earned his Ph.D. in Photogrammetry (2010-2014) from University College London, UK 📍🎓. Prior to this, he completed an M.Sc. in Photogrammetry (2004-2007) at K.N. Toosi University of Technology 🌍📐. His academic journey began with a B.Sc. in Surveying (1999-2003) from Islamic Azad University of Meybod, Iran 🇮🇷📊. Dr. Hosseininaveh’s expertise reflects his dedication to advancing geospatial sciences, contributing significantly to the field through research, education, and innovation. 🌟📡
Experience
Dr. Ali Hosseininaveh is the Head of the Photogrammetry and Remote Sensing Department at K.N. Toosi University of Technology, Tehran, Iran (2018–present) 🌍📡. Previously, he led the Close-Range Photogrammetry Lab (2014–2020) and served as an Assistant Professor (2014–2020). His teaching journey includes roles at UCL (2012–2013) 🇬🇧, Islamic Azad University, and Vali Asr University in Iran 🇮🇷. With expertise in photogrammetry, computer vision, and robotics applications, he supervises MSc and Ph.D. students while teaching diverse courses, from programming to 3D modeling. Dr. Hosseininaveh’s contributions advance geomatics education and research, blending technical precision with innovative methodologies. ✨📊🎓
Research Focus
Dr. Ali Hosseininaveh’s research primarily focuses on 3D reconstruction, computer vision, and automated monitoring systems. His work integrates dense matching algorithms, multi-image processing, and imaging network design to enhance the precision and automation of surface reconstruction, particularly for texture-less objects and construction monitoring. He emphasizes the application of low-cost systems for scalable and reliable 3D modeling in fields like photogrammetry, robotics, and autonomous systems. His innovations contribute significantly to progress monitoring, point cloud accuracy, and sensor performance evaluation for diverse applications in engineering and construction management. 🌐📸🏗️🤖📊
Technical Reports
Dr. Ali Hosseininaveh’s research focuses on photogrammetry, robotics, and 3D modeling 🌐🤖📸. His work spans producing panoramic images for 3D modeling, surveying with advanced photogrammetry techniques, and designing surveying robots. Notable projects include a photogrammetric survey of the Queen’s Staircase ceiling at Hampton Court Palace, showcasing expertise in cultural heritage preservation through technology 🏛️✨. He has also contributed to developing innovative robotic systems for surveying applications, blending engineering and precision mapping. Dr. Hosseininaveh’s research integrates cutting-edge technology with practical applications, enhancing fields like geospatial analysis, cultural documentation, and automated surveying solutions 🚀📊.
Developed software and robots
Dr. Ali Hosseininaveh’s research focus lies in 3D reconstruction, robotics, and imaging networks 🤖📸📐. His work emphasizes developing innovative tools and systems for precise image-based 3D modeling and autonomous surveying. Notable contributions include Multi-view SGM, software utilizing the Semi-Global Matching algorithm for dense 3D reconstruction; Imaging Network Designer (IND), a visual C++ tool for imaging network design; and INDRo, a robotic system integrating these designs for object photography 📷🤖. Additionally, he developed Moor, a six-wheeled autonomous robot for building reconstruction 🏗️🤖. His cutting-edge projects highlight advancements in computer vision, imaging technology, and autonomous systems. 🚀✨
Publication Top Notes
A comparison of dense matching algorithms for scaled surface reconstruction using stereo camera rigs
Automated progress controlling and monitoring using daily site images and building information modelling
An automatic 3D reconstruction system for texture-less objects
The performance evaluation of multi-image 3D reconstruction software with different sensors
Towards fully automatic reliable 3D acquisition: From designing imaging network to a complete and accurate point cloud
Imaging network design to improve the automated construction progress monitoring process
Clustering and selecting vantage images in a low-cost system for 3D reconstruction of texture-less objects