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 geo...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 almost halves the training time while achieving only a reduction of 7.4%in the average F1 score.This optimization process substantially enhances the speed and efficiency of 3D reconstruction using NeRFs.Our findings demonstrate the potential of NeRFs in detailed and realistic 3D plant reconstruction and suggest practical approaches for enhancing the speed and efficiency of NeRFs in the 3D reconstruction process.展开更多
基金supported in part by the Plant Science Institute at IowaState University,the National Science Foundation under grant number OAC:1750865the National Institute of Food and Agriculture(USDA-NIFA)as part of the AI Institute for Resilient Agriculture(AIIRA),grant number 2021-67021-35329.
文摘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 almost halves the training time while achieving only a reduction of 7.4%in the average F1 score.This optimization process substantially enhances the speed and efficiency of 3D reconstruction using NeRFs.Our findings demonstrate the potential of NeRFs in detailed and realistic 3D plant reconstruction and suggest practical approaches for enhancing the speed and efficiency of NeRFs in the 3D reconstruction process.
