PhysFlowNet: Learning Canonical Latent Manifolds via Spatio-Spectral Physics Priors for Underwater Object Detection

Underwater object detection is fundamentally ill-posed dueto severe light attenuation and scattering. These physical degradationsinduce highly non-linear geometric distortions in the visual feature space,deviating significantly from the canonical manifold of clear images andrendering standard Euclidean convolutions suboptimal. To address this,we propose PhysFlowNet, that reformulates representation rectificationas a physics-guided geometric evolution on a Riemannian manifold. Phys-FlowNet first extracts a joint spatial-frequency physical prior to encap-sulate macroscopic scattering mechanics. Conditioned on this prior, weintroduce the Parallel Physics-Residual Bottleneck (PPRB), which exe-cutes a metric-preconditioned Riemannian feature retraction to transportdistorted features back to their canonical states. Moreover, to counter-act cascaded smoothing during scale transitions, we introduce a Physics-Guided Manifold Downsampling (PMD) strategy to strictly preserve finedetail textures and structural boundaries. Ultimately, the network is op-timized via a Unified Evidential-Contrastive Objective (UECO). Com-prising a Supervised Contrastive Manifold Loss (SCML) and an Eviden-tial Uncertainty Regulation (EUR), UECO jointly ensures latent geo-metric alignment and reliable uncertainty calibration. Extensive evalua-tions across challenging underwater benchmarks demonstrate that Phys-FlowNet achieves state-of-the-art performance, establishing a principledgeometric paradigm for physically degraded vision tasks.