Road roughness is a key parameter for controlling pavement construction processes and for assessing ride quality of both paved and unpaved roads. This paper describes algorithms used in processing three-dimensional (3D) stationary terrestrial laser scanning (STLS) point clouds to obtain surface maps of point wise indices that characterize pavement roughness. The backbone of the analysis is a quarter-car model simulation over a spatial 3D mesh grid representing the pavement surface. Two case studies are presented, and results show high spatial variability in the roughness indices both longitudinally and transversely (i.e., different wheel path positions). It is proposed that road roughness characterization using a spatial framework provides more details on the severity and location of roughness features compared to the one-dimensional methods. This paper describes approaches that provide an algorithmic framework for others collecting similar STLS 3D spatial data to be used in advanced road roughness characterization.