In this study, we propose a straight-line-segment feature-extraction method for the building-fa ade point-cloud data based on slicing to improve the existing method of detecting and extracting the straight-line-segment features from the building-fa ade point-cloud data, which exhibits problems of missed detection and less-than-optimal accuracy. Further, the point cloud is sliced along the three coordinate axes after adjusting the point-cloud attitude of the building to ensure that its orientation is consistent with the Y-coordinate axis. Then, the feature points on each slice are extracted, and straight-line-segment clustering is applied to the extracted feature points based on the cylinder growth method. Finally, the straight-line-segment fitting of the feature points is performed using the 1-norm minimum residual algorithm, and the endpoints of the straight line segment are adjusted and refined. Subsequently, we validate the proposed method by applying it to several sets of experimental data; the experimental results exhibit improved accuracy, precision, and recall. The extraction accuracy of the straight line segment is half the average point spacing in the point cloud. The precision of the proposed method for extracting straight line segments is increased by 2.4% on average than that of the plane segmentation and image detection methods, whereas the recall is increased by 48.1% on average. Thus, our proposed method can accurately and effectively extract straight line segments from the building-facade point-cloud data.