Three-Dimensional Reconstruction Algorithm for Concrete Bridge Piers Based on Octree-GS and the Climbing Robot Platform

Three-dimensional (3D) reconstruction of bridge piers is critical in bridge digital inspection and digital twin technologies. To address the limitations of existing unmanned aerial vehicles (UAVs) and wall-climbing robots during bridge visual inspections, a novel annular vision acquisition system based on a climbing robot is proposed. In response to the limitations of traditional multiview stereo (MVS) algorithms and neural radiance fields (NeRF) in 3D reconstruction, this research proposes a novel Octree-Gaussian splatting (GS)-based 3D reconstruction algorithm. The algorithm learns the neural scene from multiview images represented as 3D Gaussian distributions while integrating an octree structure with level-of-detail (LOD) techniques. It adaptively queries the corresponding LOD levels from the octree based on the observation distance and scene complexity, selecting anchor points that meet the rendering requirements. Experimental results demonstrated that the proposed robotic system, combined with the 3D reconstruction algorithm, achieved state-of-the-art performance on eight bridge pier data sets, accurately reconstructing the bridge pier surface’s geometric shape and damage distribution. Furthermore, the rendering speed exceeded 40 frames per second, providing an efficient technical solution for bridge digital inspection and the generation of digital twin foundations.