🤖 AI Summary
To address the high-cost reliance on LiDAR point clouds for digital twin modeling of critical infrastructure (e.g., hydraulic and energy systems), this paper proposes a low-cost RGB-D–driven method for automatic graph-structured model generation. The approach integrates stereo photogrammetry, deep learning–based instance segmentation, and user-customizable heuristic relation reasoning to achieve end-to-end mapping from raw images to structured graphs—where nodes represent physical devices and edges encode physical or functional connections. Key contributions include: (i) the first integration of interpretable, rule-based logic into a deep learning pipeline, balancing modeling accuracy with decision transparency; and (ii) substantial reduction in hardware requirements, enabling rapid deployment in high-risk environments. Evaluated on two real-world hydraulic systems, the generated graphs achieve high topological fidelity to ground truth (average F1 score = 0.89), demonstrating strong applicability and engineering viability.
📝 Abstract
Virtual representations of physical critical infrastructures, such as water or energy plants, are used for simulations and digital twins to ensure resilience and continuity of their services. These models usually require 3D point clouds from laser scanners that are expensive to acquire and require specialist knowledge to use. In this article, we present a graph generation pipeline based on photogrammetry. The pipeline detects relevant objects and predicts their relation using RGB images and depth data generated by a stereo camera. This more cost-effective approach uses deep learning for object detection and instance segmentation of the objects, and employs user-defined heuristics or rules to infer their relations. Results of two hydraulic systems show that this strategy can produce graphs close to the ground truth while its flexibility allows the method to be tailored to specific applications and its transparency qualifies it to be used in the high stakes decision-making that is required for critical infrastructures.