Research on orthodontic treatment monitoring from oralscan video is a new direction in dental digitalization. We designed an approach to reconstruct, segment, and estimate the pose of individual teeth to measure orthodontic treatment. To handle the semantic gap in heterogeneous data on the condition that they are combined linearly, we present a multimedia interaction network (MIN) to combine heterogeneous information in point cloud segmentation by extending the graph attention mechanism. Moreover, a structure-aware quadruple loss is designed to explore the relation between multiple and diverse unmatched points in point cloud registration. The performance of our approach is evaluated on multiple tooth registration datasets, and extensive experiments show that our approach improves the accuracy by a margin of 1.4% in the inlier ratio on the Aoralscan3 dataset when it is compared with prevailing approaches.