In the context of semantic segmentation of urban scenes, the calibrated multi-views and the flatness assumption are commonly used to estimate a warped image based on the homography estimation. In order to classify planar and non-planar areas, we propose an evaluation protocol that compares several Image Quality Assessments (IQA) between a reference zone and its warped zone. We show that cosine angle distance-based measures are more efficient than euclidean distance-based for the planar/non-planar classification and that the Universal Quality Image (UQI) measure outperforms the other evaluated measures.