Detecting Points of Interest on 3D Model via Alternating Optimization Strategy

Abstract

A supervised 3D points of interest (POI) detection algorithm is proposed based on alternating optimization. Firstly, the geometric features of a 3D shape are calculated from several hand-crafted feature descriptors and used as the input of the neural network. Secondly, the biharmonic distance field is utilized to assign a label to each vertex, which is regarded as the neural network’s output. Thirdly, the complex mapping relationships between the feature vectors and the labels are learned through the neural network. Fourthly, predictions are made on the training set using the trained neural network. The differences between the predicted points of interest and ground truth are compared to further optimize the vertices’ labels, which are then used as the output to train the neural network. The third and fourth steps for alternating optimization are repeated for several times, and a neural network is finally obtained. The experimental results on the SHREC 2011 dataset show that, due to the alternate optimization strategy, our algorithm is better than the traditional methods in the key evaluation indicators FNE and FPE, and the accuracy of proposed algorithm has achieved an average improvement of more than 11%.