To address the problem of overall contour loss and missing key features in the simplified point-cloud data of steel billets, a KD-tree-guided surface-curvature-driven steel-billet point-cloud (KDSCP) simplification algorithm is proposed. First, a discrete topological relationship between points is constructed based on the KD-tree for k nearest neighbor queries. Second, the point-cloud areas are partitioned based on curvature feature thresholds. Finally, the partitioning of the steel-billet point-cloud data is simplified using adjustable simplification-rate random sampling and centroid nearest-neighbor point-simplification methods. KDSCP is compared with random sampling and improved curvature-sampling methods. The results show that KDSCP not only better preserves the main contour of the steel-billet point cloud but also achieves 17.97% and 28.70% improvements in feature-point retention, 0.4494 dB and 1.9879 dB increases in the PSNR, and 3.8791 and 2.5540 mm reductions in the Hausdorff distance at a simplification rate of 55%, respectively. The proposed KDSCP point-cloud simplification algorithm can significantly simplify steel-billet point-cloud data while maintaining complete contour and key feature information, thus benefitting the real-time processing of steel-billet point clouds.