3D object modeling with neural gas based selective densification of surface meshes

The paper proposes an automated method for the modeling of objects using multiple discrete levels of detail for virtual reality applications. The method combines classical discrete level of detail approaches with a novel solution for the creation of selectively-densified object meshes. A neural gas network is used to capture regions of interest over a sparse point cloud representing a 3D object. Meshes at different resolutions that preserve these regions are then constructed by adapting a classical simplification algorithm to allow the simplification process to affect only the regions of lower interest. Different interest point detectors are incorporated in a similar manner and compared with the proposed approach. A novel solution based on learning is proposed to select the number of faces for the discrete models of an object at different resolutions.