Haolin Pan

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About

Haolin Pan is a researcher whose work lies at the intersection of computer vision, plant phenotyping, and 3D data acquisition. His primary research focuses on developing methods for capturing and analyzing the three-dimensional structure of plants over time, with a particular emphasis on high-throughput imaging and reconstruction. Pan’s most notable contribution is the creation of the "ARABIDOPSIS 3D+T dataset" (2021), a pioneering resource that provides time-resolved 3D point clouds of Arabidopsis plants acquired twice daily. This dataset, generated using space carving from 72 images captured by a robotic arm, enables researchers to study plant growth dynamics with unprecedented spatial and temporal resolution. Although the dataset currently has 2 citations, its value lies in its potential to support future work in automated plant monitoring, growth modeling, and phenotypic analysis. Pan’s work is especially relevant for researchers in agricultural AI and computational botany, offering a foundation for developing algorithms that track morphological changes in plants over time. His contributions highlight the growing importance of 4D data in understanding biological processes.