This paper presents a study of approaches for selecting an efficient attack pose when loading piled materials with industrial construction vehicles. Automated handling of piled materials is a highly desired goal in many construction and mining applications. The main contributions of the paper are an experimental study of two novel approaches for selecting an attack pose from 3D data, compared to previously published approaches and extensions thereof. The outcome is based on quantitative validation, both with simulated data and data from a real-world scenario with nontrivial ground geometry.