RePer-360: Releasing Perspective Priors for 360° Depth Estimation via Self-Modulation

Recent depth foundation models trained on perspective im-agery achieve strong performance, yet generalize poorly to 360∘ imagesdue to the substantial geometric discrepancy between perspective andpanoramic domains. Moreover, fully fine-tuning these models typicallyrequires large amounts of panoramic data. To address this issue, wepropose RePer-360, a distortion-aware self-modulation framework formonocular panoramic depth estimation that adapts depth foundationmodels while preserving powerful pretrained perspective priors. Specifi-cally, we design a lightweight geometry-aligned guidance module to derivea modulation signal from two complementary projections (i.e., ERP andCP) and use it to guide the model toward the panoramic domain withoutoverwriting its pretrained perspective knowledge. We further introduce aSelf-Conditioned AdaLN-Zero mechanism that produces pixel-wise scalingfactors to reduce the feature distribution gap between the perspectiveand panoramic domains. In addition, a cubemap-domain consistency lossfurther improves training stability and cross-projection alignment. Byshifting the focus from complementary-projection fusion to panoramic do-main adaptation under preserved pretrained perspective priors, RePer-360surpasses standard fine-tuning methods while using only 1% of the train-ing data. Under the same in-domain training setting, it further achievesan approximately 20% improvement in RMSE. The code is available athttps://github.com/munimo/RePer360.