Intelligent manufacturing has been extensively applied in the mass production of forgings within industries such as automotive manufacturing. While defect detection has achieved a relatively high degree of automation and accuracy, the subsequent repair phase still lacks adequate intelligence. This gap primarily stems from insufficient research on accurate three-dimensional spatial localization of defects, which is critical for guiding automated repair equipment with the necessary precision. To address the issues, this paper proposes a method for spatially locating surface defects on forgings by registering complete and partial point clouds. First, a binocular vision system is employed to detect surface defects and perform 3D reconstruction of the forging, thereby acquiring its spatial coordinate system and high-resolution defect images. Subsequently, the Iterative Closest Point (ICP) registration algorithm is applied to align the coordinate systems of the complete forging point cloud with the locally reconstructed defect point cloud. Finally, the 3D spatial coordinates of the defects are determined through point cloud projection, enabling precise spatial localization of the surface defects. Experimental results demonstrate that point cloud registration achieved high-precision alignment, with RMSE values consistently maintained around 0.6 mm. The defect dimensions calculated from the resulting 3D coordinates show excellent agreement with physical measurements, with errors of merely 1.9 % in length and 3.4 % in width. This performance corroborates the feasibility and effectiveness of the proposed method for the spatial localization of surface defects in forged components.