Low Texture 3D Reconstruction System Based on Manhattan Axis and 2D/3D Line Features

Current camera pose estimation methods primarily rely on high-contrast pixels and feature points in images, but in practical applications, parallel and perpendicular artificial structures are more common. This paper proposes a low texture three-dimensional reconstruction system based on Manhattan axis and 2D/3D line features. The proposed method combines point features with commonly occurring line features in structural scenes to reduce inaccuracies in camera pose estimation caused by texture loss. By combining structural constraints, Manhattan axes, and reprojection error, the extraction of 3D lines is optimized and the robustness of camera pose estimation is improved. An improved Truncated signed distance function with implicit representation and truncated distance function is used to obtain a highly readable 3D reconstruction model. The results of pose estimation and reconstruction are compared with state-of-the-art methods on public datasets, and the proposed algorithm is shown to outperform other solutions in low-texture artificial areas.