Existing reverse-engineering methods struggle to directly generate editable, parametric CAD models from scanned data. To address this limitation, this paper proposes a reverse-modeling approach that reconstructs parametric CAD models from multi-view RGB-D point clouds. Multi-frame point-cloud registration and fusion are first employed to obtain a complete 3-D point cloud of the target object. A region-growing algorithm that jointly exploits color and geometric information segments the cloud, while RANSAC robustly detects and fits basic geometric primitives. These primitives serve as nodes in a graph whose edge features are inferred by a graph neural network to capture spatial constraints. From the detected primitives and their constraints, a high-accuracy, fully editable parametric CAD model is finally exported. Experiments show an average parameter error of 0.3 mm for key dimensions and an overall geometric reconstruction accuracy of 0.35 mm. The work offers an effective technical route toward automated, intelligent 3-D reverse modeling.