Three-dimensional (3D) shape information is valuable for fruit quality evaluation. Grading of the fruits is one of the important postharvest tasks that the fruit processing agro-industries do. Although the internal quality of the fruit is important, the external quality of the fruit influences the consumers and the market price significantly. To solve the problem of feature size extraction in 3D fruit scanning, this paper proposes an automatic fruit measurement scheme based on a 2.5-dimensional point cloud with a Kinect depth camera. For getting a complete fruit model, not only the surface point cloud is obtained, but also the bottom point cloud is rotated to the same coordinate system, and the whole fruit model is obtained by iterative closest point algorithm. According to the centroid and principal direction of the fruit, the cut plane of the fruit is made in the x-axis, y-axis, and z-axis respectively to obtain the contour line of the fruit. The experiment is divided into two groups, the first group is various sizes of pears to get the morphological parameters; the second group is the various colors, shapes, and textures of many fruits to get the morphological parameters. Comparing the predicted value with the actual value shows that the automatic extraction scheme of the size information is effective and the methods are universal and provide a reference for the development of the related application.

中文翻译：

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基于三维重构的水果形态测量

三维（3D）形状信息对于水果质量评估很有价值。水果分级是水果加工农产品加工业的重要收获后任务之一。尽管水果的内部质量很重要，但是水果的外部质量会显着影响消费者和市场价格。为了解决3D水果扫描中特征尺寸提取的问题，本文提出了一种基于Kinect深度相机的基于2.5维点云的水果自动测量方案。为了获得完整的水果模型，不仅获得了表面点云，而且将底部点云旋转到相同的坐标系，并且通过迭代最近点算法获得了整个水果模型。根据水果的质心和主要方向，x轴，y轴和z轴分别获得水果的轮廓线。实验分为两组，第一组是各种大小的梨，以获取形态参数。第二组是许多水果的各种颜色，形状和质地，以获取形态参数。将预测值与实际值进行比较表明，尺寸信息的自动提取方案是有效的，方法具有通用性，可为相关应用的开发提供参考。