Many computer graphics applications use visual saliency information to guide their treatments such as adaptive compression, viewpoint-selection, segmentation, etc. However, all these applications rest on a partial estimation of visual saliency insofar that only geometric properties of the considered 3D mesh are taken into account leaving aside the colorimetric ones. As humans, our visual attention is sensitive to both geometric and colorimetric informations. Indeed, colorimetric information modifies the eye movements while visualizing a multimedia content. We propose in this paper an innovative approach for the detection of global saliency that takes into account both geometric and colorimetric features of a 3D mesh simulating hence the Human Visual System (HVS). For this, we generate two multi-scale saliency maps based on local geometric and colorimetric patch descriptors. These saliency maps are pooled using the Evidence Theory. We show the contribution and the benefit of our proposed global saliency approach for two applications: automatic optimal viewpoint selection and adaptive denoising of 3D colored meshes.