Bird-View 3D Reconstruction for Crops with Repeated Textures
Bird-View 3D Reconstruction for Crops with Repeated Textures
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DOI：
10.1109/iros55552.2023.10341478
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发表时间：
2023-10
期刊：
影响因子：
--
通讯作者：
Guoyu Lu
中科院分区：
文献类型：
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作者：
Guoyu Lu
大规模的原地3D农田重建是一个具有挑战性的任务，因为3D作物结构在植物表型中起着至关重要的作用，并显着影响农作物的生长和产量。尽管现有的努力集中在近距离植物上，但仅针对大规模的3D作物重建明确开发了有限的基于深度学习的方法，这主要是由于缺乏大规模的作物感测数据。在本文中，我们利用无人驾驶汽车（UAV）在农业中，并利用最近被捕获的多视图现实世界中的Snap Bean Crop Dataset开发了无监督的结构，即触发框架（SFM）框架。我们的框架专为重建大规模的3D作物结构而设计。它解决了由农作物数据集中过度重复的模式引起的不准确深度推断的挑战，从而导致了高度准确的3D作物重建，以实现大规模场景。通过在作物数据集上进行的实验，我们证明了3D作物重建算法的准确性和鲁棒性。我们提出的框架的应用有可能提高农业研究，从而更好地植物表型和对作物生长和产量的理解。
Large-scale in-situ 3D reconstruction of crop fields presents a challenging task, as the 3D crop structures play a crucial role in plant phenotyping and significantly influence crop growth and yield. While existing efforts focus on close-range plants, only a limited number of deep learning-based methods have been developed explicitly for large-scale 3D crop reconstruction, mainly due to the scarcity of large-scale crop sensing data. In this paper, we leverage unmanned aerial vehicles (UAVs) in agriculture and utilize a recently captured multi-view real-world snap beans crop dataset to develop an unsupervised structure-from-motion (SfM) framework. Our framework is designed specifically for reconstructing large-scale 3D crop structures. It addresses the challenge of inaccurate depth inference caused by excessively repeated patterns in the crop dataset, resulting in highly accurate 3D crop reconstruction for large-scale scenarios. Through experiments conducted on the crop dataset, we demonstrate the accuracy and robustness of our 3D crop reconstruction algorithm. The application of our proposed framework has the potential to advance research in agriculture, enabling better plant phenotyping and understanding of crop growth and yield.