Mesh Saliency Detection via Regularized 1D CNN

3D shape saliency plays a non-negligible role in shape analysis and processing.Nearly all of the current triangular mesh saliency detection methods rely on handcrafted geometric features,while lack of flexibility.In this paper,we develop a saliency region detection method based on the feature-fusion learning which is suitable for different kinds of shapes.Starting with computing several features of the shape,the low-level features are organized in a multi-scale way and input into a 1D convolution neural network.After optimizing the central regularized loss function,we get the high-level and discriminative feature,and the prediction of saliency.Experiments on Princeton mesh dataset show that our method is applicable to specific-targeted saliency detection,has consistent effect,and outperforms existing methods both visually and quantitatively.