bibtext: @InProceedings{ Bud_Transferring_MICCAI2024,
                   author = { Budd, Charlie and Vercauteren, Tom },
                   title = { { Transferring Relative Monocular Depth to Surgical Vision with Temporal Consistency } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15006 },
                   month = {October},
                   pages = { pending },
                }
abstract: Relative monocular depth, inferring depth correct to a shift and scale from a single image, is an active research topic. Recent deep learning models, trained on large and varied meta-datasets, now provide excellent performance in the domain of natural images. However, few datasets exist which provide ground truth depth for endoscopic images, making training such models from scratch unfeasible. This work investigates the transfer of these models into the surgical domain, and presents an effective and simple way to improve on standard supervision through the use of temporal consistency self-supervision. We show temporal consistency significantly improves supervised training alone when transferring to the low-data regime of endoscopy, and outperforms the prevalent self-supervision technique for this task. In addition we show our method drastically outperforms the state-of-the-art method from within the domain of endoscopy. We also release our code, models, and ensembled meta-dataset, Meta-MED, establishing a strong benchmark for future work.
title: Transferring Relative Monocular Depth to Surgical Vision with Temporal Consistency
authors: Budd, Charlie Vercauteren, Tom