Measuring semantic traits for phenotyping is an essential but labor-intensive activity in horticulture. Researchers often rely on manual measurements which may not be accurate for tasks such as measuring tree volume. To improve the accuracy of such measurements and to automate the process, we consider the problem of building coherent three dimensional (3D) reconstructions of orchard rows. Even though 3D reconstructions of side views can be obtained using standard mapping techniques, merging the two side-views is difficult due to the lack of overlap between the two partial reconstructions. Our first main contribution in this paper is a novel method that utilizes global features and semantic information to obtain an initial solution aligning the two sides. Our mapping approach then refines the 3D model of the entire tree row by integrating semantic information common to both sides, and extracted using our novel robust detection and fitting algorithms. Next, we present a vision system to measure semantic traits from the optimized 3D model that is built from the RGB or RGB-D data captured by only a camera. Specifically, we show how canopy volume, trunk diameter, tree height and fruit count can be automatically obtained in real orchard environments. The experiment results from multiple datasets quantitatively demonstrate the high accuracy and robustness of our method.

中文翻译：

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通过合并树行的两侧重建的果园环境语义映射

测量表型的语义特征是园艺中一项必不可少但劳动密集型的活动。研究人员通常依赖手动测量，这对于测量树木体积等任务可能不准确。为了提高此类测量的准确性并使过程自动化，我们考虑了构建果园行的连贯三维 (3D) 重建的问题。尽管可以使用标准映射技术获得侧视图的 3D 重建，但由于两个部分重建之间缺乏重叠，因此合并两个侧视图是困难的。我们在本文中的第一个主要贡献是一种利用全局特征和语义信息来获得对齐两侧的初始解决方案的新方法。然后，我们的映射方法通过集成双方共有的语义信息来细化整个树行的 3D 模型，并使用我们新颖的鲁棒检测和拟合算法进行提取。接下来，我们提出了一个视觉系统，用于测量优化 3D 模型的语义特征，该模型是根据仅由相机捕获的 RGB 或 RGB-D 数据构建的。具体来说，我们展示了如何在真实果园环境中自动获取冠层体积、树干直径、树高和果实数量。来自多个数据集的实验结果定量证明了我们方法的高精度和鲁棒性。我们提出了一个视觉系统来测量优化 3D 模型的语义特征，该模型是根据仅由相机捕获的 RGB 或 RGB-D 数据构建的。具体来说，我们展示了如何在真实果园环境中自动获取冠层体积、树干直径、树高和果实数量。来自多个数据集的实验结果定量证明了我们方法的高精度和鲁棒性。我们提出了一个视觉系统来测量优化 3D 模型的语义特征，该模型是根据仅由相机捕获的 RGB 或 RGB-D 数据构建的。具体来说，我们展示了如何在真实果园环境中自动获取冠层体积、树干直径、树高和果实数量。来自多个数据集的实验结果定量证明了我们方法的高精度和鲁棒性。