To achieve the automatic reassembly (piecing) of utensil fragments, a fracture surface extraction method based on the learning of local geometric features (core focus) and a utensil reassembly method (secondary focus) are presented in this paper. The steps of the methodological framework are as follows. First, based on obtained 3D models of utensil fragments, a triangle cell descriptor is proposed to describe the geometric features of spatial neighborhoods. Second, a set of feature mapping images (FMIs) is established as a training dataset. Third, after labeling of the ground-truth data, a convolutional neural network (CNN) is trained using the FMIs. Fourth, based on processing to eliminate mislabeled triangle cells, skeletons of the fracture surface margins can be generated. Fifth, a shortcut-based strategy is proposed to eliminate residual triangle cells to extract the fracture surfaces. Sixth, a control-point- and vector-based strategy is proposed to achieve the matching and prealignment of the fracture surfaces. Finally, a cyclic error iteration strategy is designed to assemble the fragments into a holonomic utensil. This learning-based framework is more effective at extracting the key geometric data (fracture surfaces) of utensil fragments than several classical methods. It may also enable a new strategy for 3D graph processing.

为了实现器物碎片的自动重组（穿刺），本文提出了一种基于局部几何特征学习的断口提取方法（核心焦点）和器物重组方法（二次聚焦）。方法框架的步骤如下。首先，基于获得的器物碎片的3D模型，提出了三角形单元描述符来描述空间邻域的几何特征。第二，建立一组特征映射图像（FMI）作为训练数据集。第三，在标记了真实数据之后，使用FMI训练卷积神经网络（CNN）。第四，基于消除标记错误的三角形单元的处理，可以生成骨折表面边缘的骨骼。第五，提出了一种基于快捷方式的策略，以消除残留的三角形单元以提取断裂面。第六，提出了基于控制点和矢量的策略来实现断裂面的匹配和预对准。最后，设计了循环错误迭代策略，将片段组装成完整的器具。这种基于学习的框架比几种经典方法更有效地提取器皿碎片的关键几何数据（断裂表面）。它还可能启用3D图形处理的新策略。这种基于学习的框架比几种经典方法更有效地提取器皿碎片的关键几何数据（断裂表面）。它还可能启用3D图形处理的新策略。这种基于学习的框架比几种经典方法更有效地提取器皿碎片的关键几何数据（断裂表面）。它还可能启用3D图形处理的新策略。