Extreme conditions such as high temperature, strong interference, and highly reflective metal surface in industrial scenes pose significant challenges for LiDAR and imaging devices, leading to point cloud data loss and impacting data integrity. To address these challenges, this paper proposes a similarity-driven framework based on color, elevation, and normal vector gradients. The proposed framework effectively repairs point cloud holes through multi-feature fusion and pixel-level hole location, and achieves precise reconstruction of the load surface using similarity-weighted interpolation and moving least squares smoothing. Experimental results show that the proposed method improves the normalized surface height deviation value, significantly enhancing reconstruction accuracy. The proposed method also demonstrates clear advantages in processing efficiency, enabling fast completion of 3D point cloud reconstruction. The proposed method not only successfully fills point cloud holes but also preserves geometric features, overcoming data loss and surface distortion caused by reflections, thus showing remarkable performance in 3D reconstruction in high-reflectivity environments.