Polarimetric Neural Field via Unified Complex-Valued Wave Representation

Polarization has found applications in various computer vision tasks by providing additional physical cues. However, due to the limitations of current imaging systems, polarimetric parameters are typically stored in discrete form, which is non-differentiable and limits their applicability in polarization-based vision. While current neural field methods have shown promise for continuous signal reconstruction, they struggle to model the intrinsic physical interdependencies among polarimetric parameters. In this work, we propose a physics-grounded representation scheme to represent polarimetric parameters as a unified complex-valued wave. Tailored to this scheme, we propose a tuning-free fitting strategy along with a lightweight complex-valued neural network, enabling property-preserved reconstruction. Experimental results show that our method achieves state-of-the-art performance and facilitates smooth polarized image rendering and flexible resolution adjustments.