Learning-based Monocular Depth Estimation for Photogrammetric 3D Reconstruction

Abstract. Monocular depth estimation (MDE) infers depth from a single image, offering significant advantages in computational efficiency and memory consumption compared to conventional Multi-View Stereo (MVS) methods. However, most MDE methods suffer from poor multi-view geometric consistency, which limits their application to photogrammetric 3D reconstruction. To address this issue, this paper employs sparse point clouds of Structure-from-Motion (SfM) as extra geometric constraints and proposes a framework that achieves photogrammetric 3D reconstruction using off-the-shelf learning-based MDE models without the need for additional fine-tuning. Specifically, when SfM priors are available during inference, globally geometrically consistent depth maps can be directly predicted. Otherwise, the estimated monocular depths are aligned to a consistent scale using SfM results via a post-correction step. The resulting depth maps are then fused using a truncated signed distance function (TSDF) to generate dense 3D reconstructions. Experiments on photogrammetric datasets demonstrate that the proposed framework effectively improves geometric consistency across depth maps and enables high-quality scene reconstruction. In addition, we systematically analyze the impact of key parameters in depth inference and fusion, including depth map resolution, voxel size, denoising steps, and ensemble size, on reconstruction performance, and further explore the potential of MDE for photogrammetric 3D reconstruction.