用无人机多角度影像结合3DGS与SAM模型，精准估算油菜生物量。
Biomass phenotyping of oilseed rape through UAV multi-view oblique imaging with 3DGS and SAM model
- 融合3DGS与SAM，从多视角影像重建高精度3D点云并分割叶片。
- 点云体积模型预测生物量，决定系数R2达0.976，误差仅6.81%。
- 适合高通量作物表型研究者，尤其关注复杂田间环境下生物量估测。
油菜生物量估算是优化作物产量和育种策略的关键。尽管无人机成像推动了高通量表型技术发展，但现有方法多依赖正射影像，在复杂田间环境中易受叶片重叠和结构信息缺失影响。本研究结合3D高斯泼溅（3DGS）与分割一切模型（SAM），实现油菜的精确3D重建与生物量估计。利用36个角度的无人机多视角倾斜影像进行3D重建，其中SAM模块提升点云分割精度。分割后的点云转化为点云体积，并通过线性回归拟合地面实测生物量。结果表明，3DGS在7千和3万次迭代下分别达到27.43和29.53的峰值信噪比，训练时间分别为7分钟和49分钟，性能优于结构光重建（SfM）和多层次神经辐射场（Mip-NeRF）。SAM模块分割准确率高，平均交并比（mIoU）为0.961，F1分数达0.980。不同生物量提取模型对比显示，点云体积模型最优，决定系数（R²）为0.976，均方根误差（RMSE）为2.92克/株，平均绝对百分比误差（MAPE）为6.81%，显著优于地块作物体积和单株作物体积模型。该研究验证了3DGS结合多视角无人机影像在油菜生物量表型中的潜力。
原文摘要 · Abstract (English)
Biomass estimation of oilseed rape is crucial for optimizing crop productivity and breeding strategies. While UAV-based imaging has advanced high-throughput phenotyping, current methods often rely on orthophoto images, which struggle with overlapping leaves and incomplete structural information in complex field environments. This study integrates 3D Gaussian Splatting (3DGS) with the Segment Anything Model (SAM) for precise 3D reconstruction and biomass estimation of oilseed rape. UAV multi-view oblique images from 36 angles were used to perform 3D reconstruction, with the SAM module enhancing point cloud segmentation. The segmented point clouds were then converted into point cloud volumes, which were fitted to ground-measured biomass using linear regression. The results showed that 3DGS (7k and 30k iterations) provided high accuracy, with peak signal-to-noise ratios (PSNR) of 27.43 and 29.53 and training times of 7 and 49 minutes, respectively. This performance exceeded that of structure from motion (SfM) and mipmap Neural Radiance Fields (Mip-NeRF), demonstrating superior efficiency. The SAM module achieved high segmentation accuracy, with a mean intersection over union (mIoU) of 0.961 and an F1-score of 0.980. Additionally, a comparison of biomass extraction models found the point cloud volume model to be the most accurate, with an determination coefficient (R2) of 0.976, root mean square error (RMSE) of 2.92 g/plant, and mean absolute percentage error (MAPE) of 6.81%, outperforming both the plot crop volume and individual crop volume models. This study highlights the potential of combining 3DGS with multi-view UAV imaging for improved biomass phenotyping.
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