POLARIS: Polarimetric Spin-2 Equivariant Adapters for Monocular Metric Depth

Monocular depth estimation from a single RGB image is scale-ambiguous: metric scale can be recovered only through learned priors. Existing methods therefore rely on pre-trained backbones mitigating scale ambiguity partially. Polarization provides such measurements, since the angle and degree of linear polarization depend on surface orientation and thus supply geometric cues absent from intensity alone. However, the angle of polarization is measured relative to the sensor's reference axis: a camera roll shifts it in exactly the same way as a change of that axis. An encoder that ignores this transformation therefore becomes dependent on the manufacturer's arbitrary choice of reference axis leading to spin-2 angle ambiguity. To resolve the angle ambiguity, we present POLARIS, which builds a rotation-equivariant polarimetric encoder and injects its orientation-independent features into a frozen geometry backbone through a prior-preserving interface. POLARIS lowers metric AbsRel by 16% over the strongest baseline on HAMMER and ranks first or second on all metrics on GM and RPS benchmarks. Our prediction result is rotation-invariant under an arbitrary analyser-axis rotation that changes the output of a conventional encoder. As a side benefit, polarization enables POLARIS to perceive a screen showing a scene as a flat plane, while RGB-only models, including POLARIS with polarization disabled, hallucinate the depth of the displayed content. On our own real-world captures, it also recovers fine structures missed by RGB-only baselines.