Monocular per-object distance estimation with Masked Object Modeling

Aniello Panariello, Gianluca Mancusi, Fedy Haj Ali, Angelo Porrello, Simone Calderara, Rita Cucchiara

Abstract: Highlights•We introduce a novel self-supervised approach called Masked Object Modeling (MoM).•The paper proposes a novel framework for distance estimation from monocular images.•We show improved transfer capabilities for zero-shot and fine-tuning when using MoM.•We reach state-of-the-art performance on several distance estimation benchmarks.