Vectorized Grid Detection and Color Rectification for 3D Point Clouds of Photovoltaic Panels

Abstract. UAV-borne PV point clouds often suffer from severe shadow artifacts and color dropouts, limiting their use for reliable inspection and digital twin construction. We introduce a fully vectorized color rectification framework that exploits panel symmetry and 1D signal processing to restore a consistent radiometric appearance. Starting from a segmented solar-panel point cloud, the method first normalizes panel geometry via RANSAC-based plane segmentation and rotation to a canonical xy-plane, then extracts base colors by clustering RGB values to identify “panel blue” and “grid white” regions. It subsequently detects grid parameters by projecting filtered grid and panel points into 1D spatial density histograms along the x- and y-axes to estimate spacing, offset, and grid-line thickness, and finally performs vectorized recoloring and color remapping of grid and panel points using the recovered parameters. By decoupling periodic grid structure from illumination noise, our approach achieves visually near-perfect color restoration while eliminating intra-semantic variance across modules. The resulting high-fidelity, shadow-free point clouds provide a mathematically consistent foundation for PV digital twins and automated asset evaluation.