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 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.

Plant phenotyping relevance

植物の3D形状再構成にNeRFを適用し、LiDARとの比較で再構成精度を評価するとともに、学習高速化手法も提案しており、表現型取得手法が研究の中心である。

Code and data availability

The paper describes plant image/TLS datasets and NeRF analysis code, but the Data Availability Statement only promises future release ('will be made available online') with no public URL or repository identifier. The only URL present (https://poly.cam/) is a third-party capture tool, not a paper-specific asset.

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