In the current remote sensing big data era, the huge amount of data, especially the expansion of spatial dimension, brings challenges to the traditional processing and applications in data transferring, processing and visualization. Model quality assessment is usually employed to evaluate the yielding model and select the optimal simplification method, which is used to improve the efficiency of big data processing. The existing three-dimensional (3-D) terrain model assessment methods mainly exploit the data accuracy or geometric features while ignoring the impact of humans or users. We mainly investigate the quality assessment for the 3-D terrain model. The proposed method provides an integration of structural similarity and the human visual system from the multiview angles for the image quality assessment. The visual impact of observers and the structural information retention of the 3-D terrain model have been fully considered. In the experiments, two kinds of models with different simplification ratios are utilized for the terrain model evaluation. The results confirm that the proposed algorithm has a better performance of terrain model quality assessment than other traditional methods.