Detecting 3D Points of Interest Using Hierarchical Training Strategy

In this paper,we propose a novel supervised 3D points of interest(POIs)detection algorithm by using hierarchical training strategy.Firstly,the feature vectors of all the vertices of training shape are extracted,and the labeled POIs are divided into the part with sparse points and the part with dense points.Secondly,the two grouped POIs are used to train neural networks.Finally,a 3D shape POIs classifier is obtained by matching the two neural networks with the feature vectors.In the testing process,the feature vectors of all the vertices are extracted and fed to the trained classifier for prediction.An improved density peak clustering algorithm is then used to detect the POIs.Our algorithm adopts the hierarchical training strategy,which can address the issue of accurately detecting the dense POIs in the model with details.The experimental results show that our method detects the POIs with higher accuracy when compared with the traditional algorithms.Both the false positive error and false negative error are greatly reduced by using our hierarchical training method.