# Transferring Relative Monocular Depth to Surgical Vision with Temporal Consistency arXiv: https://arxiv.org/abs/2403.06683 ยท HTML: https://mlsanity.com/paper/2403.06683/ ## Key facts - Cited by 13 papers in the map (data through 14 Oct 2026). - Builds on 26 of its 32 references that are in the map. - First posted on arXiv 11 Mar 2024. ## Builds on (most cited first, 26 of 26) - [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023) - [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020) - [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020) - [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021) - [Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://mlsanity.com/paper/1907.01341/) (2019) - [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://mlsanity.com/paper/2401.10891/) (2024) - [Unsupervised Learning of Depth and Ego-Motion from Video](https://mlsanity.com/paper/1704.07813/) (2017) - [EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos](https://mlsanity.com/paper/1602.03012/) (2016) - [Consistent Video Depth Estimation](https://mlsanity.com/paper/2004.15021/) (2020) - [MiDaS v3.1 -- A Model Zoo for Robust Monocular Relative Depth Estimation](https://mlsanity.com/paper/2307.14460/) (2023) - [2017 Robotic Instrument Segmentation Challenge](https://mlsanity.com/paper/1902.06426/) (2019) - [2018 Robotic Scene Segmentation Challenge](https://mlsanity.com/paper/2001.11190/) (2020) - [Stereo Correspondence and Reconstruction of Endoscopic Data Challenge](https://mlsanity.com/paper/2101.01133/) (2021) - [Endo-Depth-and-Motion: Reconstruction and Tracking in Endoscopic Videos using Depth Networks and Photometric Constraints](https://mlsanity.com/paper/2103.16525/) (2021) - [Self-Supervised Monocular Depth and Ego-Motion Estimation in Endoscopy: Appearance Flow to the Rescue](https://mlsanity.com/paper/2112.08122/) (2021) - [Surgical-DINO: Adapter Learning of Foundation Models for Depth Estimation in Endoscopic Surgery](https://mlsanity.com/paper/2401.06013/) (2024) - [Learning How To Robustly Estimate Camera Pose in Endoscopic Videos](https://mlsanity.com/paper/2304.08023/) (2023) - [Dense Depth Estimation in Monocular Endoscopy with Self-supervised Learning Methods](https://mlsanity.com/paper/1902.07766/) (2019) - [Self-Supervised Siamese Learning on Stereo Image Pairs for Depth Estimation in Robotic Surgery](https://mlsanity.com/paper/1705.08260/) (2017) - [SERV-CT: A disparity dataset from CT for validation of endoscopic 3D reconstruction](https://mlsanity.com/paper/2012.11779/) (2020) - [FUN-SIS: a Fully UNsupervised approach for Surgical Instrument Segmentation](https://mlsanity.com/paper/2202.08141/) (2022) - [WS-SfMLearner: Self-supervised Monocular Depth and Ego-motion Estimation on Surgical Videos with Unknown Camera Parameters](https://mlsanity.com/paper/2308.11776/) (2023) - [A geometry-aware deep network for depth estimation in monocular endoscopy](https://mlsanity.com/paper/2304.10241/) (2023) - [NVDS+: Towards Efficient and Versatile Neural Stabilizer for Video Depth Estimation](https://mlsanity.com/paper/2307.08695/) (2023) - [Rapid and robust endoscopic content area estimation: A lean GPU-based pipeline and curated benchmark dataset](https://mlsanity.com/paper/2210.14771/) (2022) - [SegMatch: A semi-supervised learning method for surgical instrument segmentation](https://mlsanity.com/paper/2308.05232/) (2023) ## Built on by (most cited first, 13 of 13) - [Endo3R: Unified Online Reconstruction from Dynamic Monocular Endoscopic Video](https://mlsanity.com/paper/2504.03198/) (2025) - [Point Tracking in Surgery--The 2024 Surgical Tattoos in Infrared (STIR) Challenge](https://mlsanity.com/paper/2503.24306/) (2025) - [Zero-shot Monocular Metric Depth for Endoscopic Images](https://mlsanity.com/paper/2509.18642/) (2025) - [Learning to Efficiently Adapt Foundation Models for Self-Supervised Endoscopic 3D Scene Reconstruction from Any Cameras](https://mlsanity.com/paper/2503.15917/) (2025) - [Endoscopic Depth Estimation Based on Deep Learning: A Survey](https://mlsanity.com/paper/2507.20881/) (2025) - [More than Segmentation: Benchmarking SAM 3 for Segmentation, 3D Perception, and Reconstruction in Robotic Surgery](https://mlsanity.com/paper/2512.07596/) (2025) - [Online camera-pose-free stereo endoscopic tissue deformation recovery with tissue-invariant vision-biomechanics consistency](https://mlsanity.com/paper/2506.19388/) (2025) - [X-RAFT: Cross-Modal Non-Rigid Registration of Blue and White Light Neurosurgical Hyperspectral Images](https://mlsanity.com/paper/2507.07747/) (2025) - [DeLightMono: Enhancing Self-Supervised Monocular Depth Estimation in Endoscopy by Decoupling Uneven Illumination](https://mlsanity.com/paper/2511.20058/) (2025) - [EndoDDC: Learning Sparse to Dense Reconstruction for Endoscopic Robotic Navigation via Diffusion Depth Completion](https://mlsanity.com/paper/2602.21893/) (2026) - [Confidence-aware Monocular Depth Estimation for Minimally Invasive Surgery](https://mlsanity.com/paper/2603.03571/) (2026) - [Spectral Rectification for Parameter-Efficient Adaptation of Foundation Models in Colonoscopy Depth Estimation](https://mlsanity.com/paper/2603.15374/) (2026) - [Point Tracking in Surgery--The 2025 Surgical Tattoos in Infrared Challenge (STIRC2025)](https://mlsanity.com/paper/2607.12939/) (2026) ## Abstract (arXiv) Relative monocular depth, inferring depth up to 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, model and ensembled meta-dataset, Meta-MED, establishing a strong benchmark for future work.