In size, weight, and power (SWaP)-constrained optical systems, such as spaceborne LiDAR, high-precision centroid localization often relies on focal-plane measurements without dedicated wavefront sensors. Under such conditions, the nonlinear coupling between optical aberrations and sensor noise introduces systematic bias that is difficult to mitigate using conventional centroiding methods. To address this issue, we propose a physics-conditioned feature correction framework based on an aberration-conditioned attention mechanism. A hybrid CNN–Transformer architecture is employed to predict and compensate for systematic centroid bias. Specifically, convolutional layers encode the degraded spot morphology, while a multi-head attention mechanism leverages Seidel aberration coefficients to adaptively modulate spatial features for precise regression. Given the unavailability of absolute ground-truth coordinates in empirical scenarios, a physics-consistent simulation framework based on scalar diffraction theory is constructed to generate synthetic data for supervised learning. Simulation results indicate that the proposed method objectively reduces anisotropic systematic bias, achieving a localization root-mean-square error (RMSE) of 0.011 to 0.021 pixels, and maintains stable sub-pixel accuracy even under a 10% empirical prior perturbation. To evaluate generalization performance and engineering reliability, a wedge-based double-spot platform is developed to verify physical consistency via geometric invariance. Experimental results demonstrate a measured spacing standard deviation (SD) of 0.015 to 0.039 pixels. This validates the framework’s transferability from theoretical simulation to controlled physical measurements, providing an algorithmic foundation for precision optical metrology in hardware-constrained environments.
Zhao et al. (2026) studied this question.