The Research On 3D Model Semantic Segmentation Algorithm

In last decades, 3D mesh reconstruction and visualization technology has an increasing development. With more advanced an accurate technology the equipment is employed, larger scale the 3d models have. More difficult of real-time processing is increasing with the enlarged data and complexity of model. Semantic segmentation is a curial process in computer animation. Many researches has been done, such as parameter, compression, morphing, which mostly using model segmentation algorithm as a preliminary stage. For mesh segmentation can greatly reduce the complexity of the algorithms.3D model segmentation became hot research topic in recent years. This paper presents a semantic segmentation algorithm under the guidance of skeleton of 3D humanoid model. The algorithm is in two phase: (1) extract the skeleton of humanoid model; (2) identify the feature points of the extracted skeleton and segment the model according the key point employ the short-cut rule of cross section.The main contributions of our work are:1) Research on the minima rule and shortcut rule, this paper analyzes the advantages and limitations of the minima rule and shortcut rule in detail through a lot of specific examples.2) This paper presents a semantic segmentation algorithm under the guidance of skeleton of 3D humanoid model. This paper extraction the skeleton line of the model using a curve-skeleton extraction algorithm combining distance transform and potential field, employ pre-knowledge of anthropometry to identification the semantic feature points of skeleton line which assures the correctness of semantic segmentation, finish the semantic segmentation of 3D model using the feature points based on visual cognitive psychology. Comparing with the semantic segmentation algorithm of 3d model in Li's paper, the calculation efficiency of this algorithm is increased greatly.