Speckle-Assisted Binocular 3D Reconstruction of Asphalt Pavement with a Multi-Scale Adaptive Feature Fusion Algorithm

Abstract

To address the challenges of unreliable feature matching, high mismatch rates, and limited reconstruction accuracy in binocular stereo vision applied to asphalt pavement with inherent weak texture features, this paper proposes a speckle-assisted binocular 3D reconstruction method based on a multi-scale adaptive feature fusion algorithm. Infrared speckle patterns are actively projected to enrich the pavement surface features, and a multi-scale matching framework is developed by integrating Laplacian pyramid representations, feature-driven adaptive regularization, and Softmax-based nonlinear fusion. This design can achieve stable and accurate disparity estimation, even in weak texture regions, and produce high-quality 3D point clouds that faithfully represent both macro-scale undulations and micro-scale texture details. Ablation experiments validate the effectiveness of the proposed modules, showing that the relative errors of the arithmetic mean height (Sa) and root-mean-square height (Sq) are reduced to below 2.3%. When aligned with 3D scanner data using the iterative closest point (ICP) algorithm, the reconstructed point clouds achieve sub-millimeter mean error and an overlap rate exceeding 97%. The results indicate that the proposed method offers a reliable technical solution for efficient texture-depth analysis and practical pavement condition assessment.