A 3D reconstruction method for substations based on superpixel segmentation and point cloud registration

Abstract To address the challenges of blurred equipment boundaries and cumulative registration errors from multiple viewpoints in 3D reconstruction of substations, this study proposes a method based on superpixel segmentation and point cloud registration. Laser radar and stereo cameras are used to synchronously capture images of the substation scene. The K-means clustering algorithm, which does not require predefined cluster numbers, is employed to extract key frames from the visible light images, thereby reducing image redundancy. The key frames are processed using a graph-based superpixel segmentation algorithm to obtain semantically coherent superpixel sets. Combined with a plane estimation algorithm, effective superpixels with sufficient projection points are identified. The superpixel blocks are accurately segmented using the reprojection error of semi-dense point clouds from neighboring key frame superpixel images. The ICP algorithm is employed to achieve precise registration between the laser point cloud and the superpixel segmentation results. Based on the point cloud registration results, we complete the 3D reconstruction of the substation using Delaunay triangulation and point-by-point interpolation. Experimental results show that the key structure loss rate of the 3D reconstruction of the substation is below 0.1%, with high scene completeness and detail restoration, effectively supporting the digital operation and maintenance of substations.