Effective point cloud denoising is critical in 3D plant phenotyping applications, which reduces interference in subsequent algorithms and improves the accuracy of plant phenotypes measurement. Deep learning-based point cloud denoising algorithms have shown excellent denoising performance on simple CAD models. However, these algorithms suffer from issues including over-smoothing or shrinkage and low efficiency when applied on density uneven, incomplete, various types of noise and complex plant point clouds. We proposed a plant point cloud denoising network (PDN) based on point cloud density gradient field learning, which can effectively address the challenges posed by plant point clouds. PDN consists of three main modules: point density feature (PDF) exception module, umbrella operator feature (UOF) computation module, and point density gradient (DG) estimation module. The performance of PDN was evaluated in experiments using point clouds of multiple plant species with noise of different types. Under different levels of Gaussian noise, our method achieved a relative performance improvement of 7.6%-19.3% compared to the state-of-the-art baseline methods, reaching state-of-the-art denoising performance. For noise of different types, the majority of our denoising results outperformed the baseline methods. In addition, our method was 0.5 and 8.6 times faster than the baseline methods when processing point clouds with low and high noise level, respectively. The good robustness, generalization, and computational efficacy of PDN are expected to facilitate the acquisition of high-precision 3D point clouds for various plant species, enhance the versatility of 3D phenotyping methods, improve the accuracy of the measurement of structural phenotypes, and increase the throughput of data processing, therefore facilitate the development of modern breeding research. The source code and the datasets used in this study is available on GitHub at .

中文翻译：

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Plant-Denoising-Net (PDN)：基于密度梯度场学习的植物点云去噪网络

有效的点云去噪在3D植物表型应用中至关重要，它可以减少后续算法的干扰，提高植物表型测量的准确性。基于深度学习的点云去噪算法在简单的CAD模型上表现出了优异的去噪性能。然而，这些算法在应用于密度不均匀、不完整、各类噪声和复杂植物点云时，存在过度平滑或收缩、效率低下等问题。我们提出了一种基于点云密度梯度场学习的植物点云去噪网络（PDN），可以有效解决植物点云带来的挑战。 PDN由三个主要模块组成：点密度特征（PDF）异常模块、伞算子特征（UOF）计算模块和点密度梯度（DG）估计模块。 PDN 的性能在实验中使用具有不同类型噪声的多种植物物种的点云进行了评估。在不同水平的高斯噪声下，我们的方法与state-of-the-art的基线方法相比实现了7.6%-19.3%的相对性能提升，达到了state-of-the-art的去噪性能。对于不同类型的噪声，我们的大多数去噪结果都优于基线方法。此外，在处理低噪声水平和高噪声水平的点云时，我们的方法分别比基线方法快 0.5 倍和 8.6 倍。 PDN良好的鲁棒性、泛化性和计算效能有望促进多种植物物种高精度3D点云的获取，增强3D表型方法的通用性，提高结构表型测量的准确性，并增加数据处理的吞吐量，从而促进现代育种研究的发展。本研究中使用的源代码和数据集可在 GitHub 上获取，网址为 。