Monocular Modeling of Non-Cooperative Space Targets Under Adverse Lighting Conditions

Accurate modeling of non-cooperative space targets remains a significant challenge, particularly under complex illumination conditions. A hybrid virtual–real framework is proposed that integrates photometric compensation, 3D reconstruction, and visibility determination to enhance the robustness and accuracy of monocular-based modeling systems. To overcome the breakdown of the classical photometric constancy assumption under varying illumination, a compensation-based photometric model is formulated and implemented. A point cloud–driven virtual space is constructed and refined through Poisson surface reconstruction, enabling per-pixel depth, normal, and visibility information to be efficiently extracted via GPU-accelerated rendering. An illumination-aware visibility model further distinguishes self-occluded and shadowed regions, allowing for selective pixel usage during photometric optimization, while motion parameter estimation is stabilized by analyzing angular velocity precession. Experiments conducted on both Unity3D-based simulations and a semi-physical platform with robotic hardware and a sunlight simulator demonstrate that the proposed method consistently outperforms conventional feature-based and direct SLAM approaches in trajectory accuracy and 3D reconstruction quality. These results highlight the effectiveness and practical significance of incorporating virtual space feedback for non-cooperative space target modeling.