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we built a monocular depth estimation pipeline that is both metrically accurate as well as generalizable we leverage a combination of visual data from a monocular camera feed in conjunction with inertial data from inertial measurement units our visual inertial depth estimation pipeline uses inertial data to resolve metric scale in a fine invariant depth estimates our modular approach is compatible with existing monocular depth estimation models and Vio systems for instance with depth models like dense prediction Transformers and with office shell visual inertial systems like Vince mono we first perform Global alignment by fitting monocular depth output against available metric sparse def points we then construct a scale map scaffolding by interpolating between metric scale factors that have been computed at known sparse points our lightweight Scale map learner Network refines the scale map scaffolding so that globally aligned depth can be locally realigned at every individual pixel thi