Abstract
Mesh saliency, which measures the perceptual importance of different regions on a mesh, benefits a wide range of applications. However, existing mesh saliency models are largely built with hard-coded formulae, which cannot capture true human perception. Some existing techniques utilise indirect measures to capture user perception (e.g., mouse clicks), which can be unreliable. In this work, we collect eye-tracking data for 3D objects seen from different views, and develop an optimisation-based approach to fusing heat-maps captured from individual views to form consistent saliency maps on meshes. To predict mesh saliency on a new shape, we further develop a learning-based approach that regresses local surface characteristics based on a set of input features. Experimental results show that our learning-based method achieves better performance than state-of-the-art methods for unseen shapes. We will make our dataset publicly available.