Evaluating Neural Radiance Fields for 3D Plant Geometry Reconstruction in Field Conditions.

ABSTRACT: We evaluate different Neural Radiance Field (NeRF) techniques for the 3D reconstruction of plants in varied environments, from indoor settings to outdoor fields. Traditional methods usually fail to capture the complex geometric details of plants, which is crucial for phenotyping and breeding studies. We evaluate the reconstruction fidelity of NeRFs in 3 scenarios with increasing complexity and compare the results with the point cloud obtained using light detection and ranging as ground truth. In the most realistic field scenario, the NeRF models achieve a 74.6% F1 score after 30 min of training on the graphics processing unit, highlighting the efficacy of NeRFs for 3D reconstruction in challenging environments. Additionally, we propose an early stopping technique for NeRF training that alm

SUBMITTER: Arshad MA 

PROVIDER: S-EPMC11382571 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature