3D Reconstruction of Plant Leaves for High-Throughput Phenotyping
3D Reconstruction of Plant Leaves for High-Throughput Phenotyping
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DOI：
10.1109/bigdata.2018.8622428
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发表时间：
2018-12
期刊：
影响因子：
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通讯作者：
Feiyu Zhu;S. Thapa;T. Gao;Y. Ge;H. Walia;Hongfeng Yu
中科院分区：
文献类型：
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作者：
Feiyu Zhu;S. Thapa;T. Gao;Y. Ge;H. Walia;Hongfeng Yu
生成植物的三维数字表示对于研究人员获得植物动力学的详细了解是必不可少的。新兴的高通量植物表型技术可以捕获植物点云，然而，这些点云通常包含缺陷，并且使其成为生成精确3D重建的更换任务。我们提出了一个端到端的管道，从玉米和水稻植物的点云重建表面。特别是，我们提出了一种两步聚类方法，根据玉米和水稻的特性准确地分割每个单个植物成分的点。我们进一步使用表面拟合和边缘拟合来确保所得表面的平滑性。通过后期处理，包括纹理和照明，获得逼真的可视化效果。我们的实验研究已经探索了参数空间，并证明了我们的高通量植物表型管道的有效性。
Generating 3D digital representations of plants is indispensable for researchers to gain a detailed understanding of plant dynamics. Emerging high-throughput plant phenotyping techniques can capture plant point clouds that, however, often contain imperfections and make it a changeling task to generate accurate 3D reconstructions. We present an end-to-end pipeline to reconstruct surfaces from point clouds of maize and rice plants. In particular, we propose a two-step clustering approach to accurately segment the points of each individual plant component according to maize and rice properties. We further employ surface fitting and edge fitting to ensure the smoothness of resulting surfaces. Realistic visualization results are obtained through post-processing, including texturing and lighting. Our experimental study has explored the parameter space and demonstrated the effectiveness of our pipeline for high-throughput plant phenotyping.