@inproceedings{McAfee2025,
title  =  {Evaluation of 3D Gaussian Splatting in Plant Reconstruction},
author = {Aaron McAfee and Thomas Pluck and Rozenn Dahyot and Gerry Lacey},
booktitle  =  {Irish Machine Vision and Image Processing (IMVIP 2025)},
address =  {Ulster University, Derry-Londonderry, Northern Ireland},
volume  =  {},
year  =  {2025},
pages={26-29},
abstract  =  {Accurate 3D reconstruction of plants is essential for applications in precision agriculture, phenotyping,
and plant health monitoring. Traditional methods such as LiDAR or Structure-from-Motion often struggle
with complex plant topologies or require complex sensing hardware. Recent advances in neural rendering,
particularly 3D Gaussian Splatting (3DGS), have shown promise in efficiently capturing fine-grained plant
details. In this paper, we evaluate the performance of 3DGS on a new dataset of seven plants, each comprising
approximately 500 multi-view images. Results demonstrate that 3DGS effectively reconstructs complex
plant structures and is a suitable visual aid for plant phenotyping experts.},
url  =  {Paper=https://pure.ulster.ac.uk/ws/portalfiles/portal/227817636/FinalProceedings_v1.1.pdf 
Code=https://github.com/aaronmcafee123/SynthPlant3D},
doi={10.21251/8043511f-bf93-4b36-9348-0726af0987f6},
keywords={3D Gaussian Splatting, Precision Agriculture, Plant Phenotyping, 3D Reconstruction},
note  =  {},
}

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