3D model semantic automatic annotation based on weak label

With the increasing popularity of 3D applications, a lot of 3D geometry models are being created. The purpose of annotation for 3D model is that it can automatically list the best suitable labels to describe the 3D models; it is an important part of the text-based 3D model retrieval. A novel method LPMLL for 3D models multiple semantic annotation was proposed based on weak label. First, a graph-based method was proposed to expand the labeled data set. Then, a multi-label lazy learning approach was proposed based on its k nearest neighbors, and maximum a posteriori (MAP) principle was utilized to determine the label set for the unseen 3D model. Experimental evaluation of the method shows that the proposed method is effective in autotagging 3D models.