Metric Scaling and Extrinsic Calibration of Monocular Neural Network-Derived 3D Point Clouds in Railway Applications

Three-dimensional reconstruction using monocular camera images is a well-established research topic. While multi-image approaches like Structure from Motion produce sparse point clouds, single-image depth estimation via machine learning promises denser results. However, many models estimate relative depth, and even those providing metric depth often struggle with unseen data due to unfamiliar camera parameters or domain-specific challenges. Accurate metric 3D reconstruction is critical for railway applications, such as ensuring structural gauge clearance from vegetation to meet legal requirements. We propose a novel method to scale 3D point clouds using the track gauge, which typically only varies in very limited values between large areas or countries worldwide (e.g., $1.435$ m in Europe). Our approach leverages state-of-the-art image segmentation to detect rails and measure the track gauge from a train driver’s perspective. Additionally, we extend our method to estimate a reasonable railway-specific extrinsic camera calibration. Evaluations show that our method reduces the average Chamfer distance to LiDAR point clouds from $1.94$ m (benchmark UniDepth) to $0.41$ m for image-wise calibration and $0.71$ m for average calibration.