MambaFourier: A Physics-Inspired Spectral Fusion Model for Low-Light Event–RGB Semantic Segmentation

Low-light perception remains a fundamental bottleneck, as insufficient brightness causes texture to collapse and appearance-based segmentation to fail catastrophically. Event cameras remain informative in these regimes, yet existing RGB-Event fusion modules are generic spatial or token mixers that ignore the modalities' spectral asymmetry: events are sparse high-frequency temporal derivatives of log intensity, whereas frames carry reliable low-frequency structure but noise-dominated high frequencies under low light. We introduce MambaFourier, a dual-branch state-space segmentation network whose fusion operator is physics-inspired. At each scale, a Wave-Fourier Fusion (WFF) block treats the two feature maps as coupled damped wave fields. Since the Laplacian is diagonal in the Fourier basis, the damped wave equation has a closed-form per-wavenumber solution: WFF performs one real FFT, applies a learnable per-channel wave propagator, a band-limited free-form filter, and a reciprocal 2x2 cross-modal coupling, then one inverse FFT. Spectral parameters are indexed by mode, enabling resolution transfer, and WFF is an exact identity at initialisation. MambaFourier reaches 79.39% mIoU on DDD17 (+1.83% over MambaSeg), 75.65% on DSEC-Semantic (+4.61% over ESC), 70.40% on DERS-XS (+3.30% over ESC), and 56.51% on DSEC-Xtrm (+5.64% over the previous best). Under synthetic rain and fog on DSEC, it attains 73.7% mIoU versus 72.5% for MambaSeg.