To overcome the low efficiency of traditional methods in detecting construction building flatness and the considerable influence of human factors on these detection results, this study proposes a flatness-detection method based on three dimensional (3D) laser scanning. First, a 3D laser scanner was used to collect, process, and stitch the data related to the target building to obtain high-precision 3D point-cloud data. Second, based on the characteristics of the building flatness, a nonuniform thinning method was designed to preserve the concave and convex characteristics of the wall. Third, the random sampling consistency algorithm and the eigenvalue method were used to automatically extract and fit point-cloud data related to the building to obtain the geometric parameters of each wall to be detected. Finally, a flatness-detection method for construction buildings employing 3D laser scanning was designed based on the topological-spatial relationship between a fitting plane and the point cloud data. The results of this study show that the proposed nonuniform thinning method can effectively realize the thinning of point-cloud data. In addition, the data thinning ratio reaches 55.4 % and the concave and convex characteristics of the wall surface can be preserved without loss. Furthermore, the proposed flatness detection method is theoretically feasible, exhibits reliable accuracy, and achieves a detection efficiency that is 23.33% higher compared with that achieved by traditional detection methods.