Mesh simplification is an important problem in computer graphics. Given a polygonal mesh, the goal is to generate another mesh which approximates the underlying shape but includes less polygons, edges and vertices. Early methods focused only on preserving the overall shape of the geometric model, whereas current methods also handle meshes with attributes (normal vectors, colors, texture coordinates) so that both the mesh shape and the mesh appearance are preserved. The goal of this master thesis is to develop, implement and test a mesh simplification algorithm able to simplify large models in‐core using a vertex clustering algorithm. Several detail‐preserving techniques will be examined and implemented and a new filter is proposed, taking into account geometry features and nodal defined attributes. We also review recent advances in spatial hash tables to achieve a more compact storage, and we analyze and evaluate their impact in the simplification process.