A 3D reconstruction method leveraging line laser point cloud processing for complex surfaces is proposed to address the challenges associated with the Fuxing track door, including its irregular surface shape, uneven substrate, and inefficient manual inspection. First, an enhanced image subtraction algorithm utilizing sub-pixel extension is designed to increases image resolution 16-fold during line structured light extraction, thereby mitigate detail loss. Second, a point cloud data preprocessing technique that employs combined filtering is introduced to enhance processing efficiency. Finally, a dimensionality reduction and geo-classification simplification algorithm for 3D data is developed to enhance program efficiency while simultaneously extracting valuable edge information. Experimental results demonstrate that the 3D model achieves an average curvature fitting rate of 93%, indicating high precision.