Isotropic PSF modification for optimized optical lens design in image-matching tasks.

Image matching is widely used in many practical applications, such as high-precision map reconstruction, UAV localization, and autonomous driving. Since the optical lens directly influences the captured image, which in turn affects image-matching performance, designing optical lenses that are better suited for image-matching tasks under similar budget or size constraints is a highly valuable and practical problem. In this paper, based on the differentiable optics framework, we propose modifying the point spread function (PSF) of the optical lens to be isotropic across different fields of view (FoV) by introducing intuitive PSF-based loss functions. This modification benefits feature detection and description performance. Experimental results show that our method significantly improves the feature-matching performance of the given optical design, even though it does not achieve the highest image quality.