Evaluating Neural Radiance Fields (NeRFs) for 3D Plant Geometry Reconstruction in Field Conditions

Abstract

We evaluate different Neural Radiance Fields (NeRFs) 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 three scenarios with increasing complexity and compare the results with the point cloud obtained using LiDAR as ground truth. In the most realistic field scenario, the NeRF models achieve a 74.6% F1 score after 30 minutes of training on the GPU, 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 significantly 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形状再構成を対象にNeRF手法を評価し、LiDARを基準とした精度比較と早期停止による高速化を検証しているため、植物表現型取得法が中心である。

Code and data availability

The paper describes a plant image/TLS dataset and NeRF evaluation framework, but the supplied blocks contain no public deposit, availability statement, or authors' URL for the dataset, code, or models. The only URL present (poly.cam) is a commercial capture app cited as a tool, not a paper-specific asset.

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