Neil Dixon
Papers
1
Total Citations
26
H-Index
1
About
Dr Neil Dixon is a leading figure in geomatics and photogrammetry, whose research centres on the automated registration of 3D models derived from Structure-from-Motion (SfM) and Multi-View Stereo (MVS) techniques. His major contribution lies in developing robust, automated workflows for aligning multitemporal terrestrial and oblique aerial imagery, a critical step for accurate change-detection analysis in environmental monitoring and infrastructure assessment. His most-cited work, "Automated Registration of SfM‐MVS Multitemporal Datasets Using Terrestrial and Oblique Aerial Images" (2021, 26 citations), rigorously evaluates the use of the Scale-Invariant Feature Transform (SIFT) algorithm within modern photogrammetric software, demonstrating a practical, repeatable method for co-registering complex datasets. This work has provided a foundational framework for researchers and practitioners seeking to automate the often laborious process of aligning 3D point clouds over time. Dixon’s research directly addresses the challenge of ensuring geometric consistency in diachronic studies, enabling more reliable quantification of surface change. His achievements include advancing the operational use of SfM-MVS in fields such as geomorphology and civil engineering, where precise temporal comparisons are essential. Through his methodical approach, Dixon has significantly enhanced the accuracy and efficiency of multitemporal 3D analysis, making him a respected voice in the photogrammetry community.
Research Focus
Key Achievements
Top Papers
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