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This paper presents FORGE-SIM, a novel method for reconstructing multi-patch B-spline boundary representations from sparse RGB images, addressing the limitations of traditional image-based reconstructions that lack the necessary properties for numerical simulation. By optimizing the spline representation, the method produces compact, smooth, and watertight geometries compatible with CAD and simulation workflows, and allows for the projection of observation-derived fields onto these models. The results demonstrate that the reconstructed geometries are suitable for thermal simulation and modal analysis, effectively bridging the gap between computer vision and numerical analysis.
FORGE-SIM transforms sparse image data into simulation-ready geometries, enabling seamless integration of computer vision and numerical analysis.
Image-based reconstruction aims to recover three-dimensional geometry from images. Recent advances have enabled the recovery of visually detailed models, yet their representations are not well-suited for numerical simulation. Simulation frameworks typically require explicit, watertight, and smooth geometries to ensure numerical robustness and accuracy, properties that surfaces extracted from image-based reconstructions lack. We propose FORGE-SIM, a method to directly reconstruct a multi-patch B-spline boundary representation from sparse posed RGB images without manual intervention. By optimizing the spline representation itself, our approach produces compact, smooth, and watertight geometries that are natively compatible with both Computer Aided Design and simulation workflows. Additionally, we introduce a strategy to project observation-derived fields, such as a thermal state and semantic information, onto the reconstructed models in the same spline basis, enabling immediate use in simulation. We demonstrate that the obtained models are of sufficiently high quality to enable thermal simulation and modal analysis. By unifying image-based reconstruction and simulation-ready modeling within a single optimization framework, this work removes a long-standing barrier between computer vision and numerical analysis. We anticipate that it will enable new workflows for simulation-driven design, inspection, and digital twin applications.
