Plant roots are the main conduit to its interaction with the physical and biological environment. A 3D root system architecture can provide fundamental and applied knowledge of a plant’s ability to thrive, but the construction of 3D structures for thin and complicated plant roots is challenging. Existing methods such as structure-from-motion and shape-from-silhouette require multiple images, as input, under a complicated optimization process, which is usually not convenient in fieldwork. Little effort has been put into investigating the applications of deep neural network methods to reconstruct thin objects, like plant root systems, from a single image. We propose an unsupervised learning scheme to estimate the root depth from only one image as input, which is further applied to reconstruct the complete root system. The boundaries of the reconstructed object usually contain large errors, which is a significant problem for roots with many thin branches. To reduce reconstruction errors, we integrate a cross-view GAN-based network into the reconstruction process, which predicts the root image from a different perspective. Based on the predicted view, we reconstruct the root system using stereo reconstruction, which helps to identify the accurately reconstructed points by enforcing their consistency. The results on both the real plant root dataset and the synthetic dataset demonstrate the effectiveness of the proposed algorithm compared with state-of-the-art single image 3D reconstruction models on plant roots.

中文翻译：

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用于单图像植物根系重建的同步直接深度估计和合成立体

植物根系是其与物理和生物环境相互作用的主要渠道。3D 根系体系结构可以提供植物茁壮成长能力的基础知识和应用知识，但为细而复杂的植物根系构建 3D 结构具有挑战性。现有的方法，例如从运动中提取结构和从剪影中提取形状，需要在复杂的优化过程中将多个图像作为输入，这在现场工作中通常是不方便的。在研究深度神经网络方法从单个图像重建薄物体（如植物根系）的应用方面几乎没有付出任何努力。我们提出了一种无监督学习方案，仅从作为输入的一张图像中估计根深度，并进一步应用于重建完整的根系统。重建对象的边界通常包含较大的误差，这对于具有许多细分支的根来说是一个重大问题。为了减少重建错误，我们将基于交叉视图 GAN 的网络集成到重建过程中，从不同的角度预测根图像。基于预测的视图，我们使用立体重建来重建根系统，这有助于通过加强它们的一致性来识别准确重建的点。与最先进的植物根单图像 3D 重建模型相比，真实植物根数据集和合成数据集的结果证明了所提出算法的有效性。我们将基于交叉视图 GAN 的网络集成到重建过程中，从不同的角度预测根图像。基于预测的视图，我们使用立体重建来重建根系统，这有助于通过加强它们的一致性来识别准确重建的点。与最先进的植物根单图像 3D 重建模型相比，真实植物根数据集和合成数据集的结果证明了所提出算法的有效性。我们将基于交叉视图 GAN 的网络集成到重建过程中，从不同的角度预测根图像。基于预测的视图，我们使用立体重建来重建根系统，这有助于通过加强它们的一致性来识别准确重建的点。与最先进的植物根单图像 3D 重建模型相比，真实植物根数据集和合成数据集的结果证明了所提出算法的有效性。