用3D高斯点云实现田间小麦穗的自动三维重建与表型分析。
Wheat3DGS: In-field 3D Reconstruction, Instance Segmentation and Phenotyping of Wheat Heads with Gaussian Splatting
- 结合3DGS与SAM模型，实现小麦穗的精确三维实例分割。
- 对穗长、宽、体积的误差分别为15.1%、18.3%、40.2%，优于NeRF和传统方法。
- 可大规模非破坏性测量产量相关性状，适合育种与田间表型研究。
自动化提取植物形态特征对于支持作物育种和农业管理中的高通量田间表型（HTFP）至关重要。基于多视角RGB图像的方案因其可扩展性和低成本而备受关注，能够实现二维方法无法直接获取的体积分量。尽管神经辐射场（NeRFs）等先进方法已展现出潜力，但其应用仍局限于少数植株或器官的计数与性状提取。此外，由于田间环境下植株密集、遮挡严重，准确测量单个小麦穗这一关键产量结构仍极具挑战。近期发展的3D高斯点云（3DGS）为HTFP提供了新可能，具备高质量重建与显式点表示优势。本文提出Wheat3DGS，首次将3DGS应用于HTFP，结合分割一切模型（SAM），实现对数百穗小麦的自动三维实例分割与形态测量。通过与高分辨率激光扫描数据对比，各实例的平均绝对百分比误差分别为：长度15.1%、宽度18.3%、体积40.2%。相较于基于NeRF的方法和传统多视图立体（MVS），结果更优。本方法实现了快速、非破坏性的大规模关键产量性状测量，显著推动作物育种进程并深化对小麦发育的理解。
原文摘要 · Abstract (English)
Automated extraction of plant morphological traits is crucial for supporting crop breeding and agricultural management through high-throughput field phenotyping (HTFP). Solutions based on multi-view RGB images are attractive due to their scalability and affordability, enabling volumetric measurements that 2D approaches cannot directly capture. While advanced methods like Neural Radiance Fields (NeRFs) have shown promise, their application has been limited to counting or extracting traits from only a few plants or organs. Furthermore, accurately measuring complex structures like individual wheat heads-essential for studying crop yields-remains particularly challenging due to occlusions and the dense arrangement of crop canopies in field conditions. The recent development of 3D Gaussian Splatting (3DGS) offers a promising alternative for HTFP due to its high-quality reconstructions and explicit point-based representation. In this paper, we present Wheat3DGS, a novel approach that leverages 3DGS and the Segment Anything Model (SAM) for precise 3D instance segmentation and morphological measurement of hundreds of wheat heads automatically, representing the first application of 3DGS to HTFP. We validate the accuracy of wheat head extraction against high-resolution laser scan data, obtaining per-instance mean absolute percentage errors of 15.1%, 18.3%, and 40.2% for length, width, and volume. We provide additional comparisons to NeRF-based approaches and traditional Muti-View Stereo (MVS), demonstrating superior results. Our approach enables rapid, non-destructive measurements of key yield-related traits at scale, with significant implications for accelerating crop breeding and improving our understanding of wheat development.
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