# EndoDAC: Efficient Adapting Foundation Model for Self-Supervised Depth Estimation from Any Endoscopic Camera

arXiv: https://arxiv.org/abs/2405.08672 · HTML: https://mlsanity.com/paper/2405.08672/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 34 references that are in the map.
- First posted on arXiv 14 May 2024.

## Builds on (most cited first, 19 of 19)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [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)
- [How Do Vision Transformers Work?](https://mlsanity.com/paper/2202.06709/) (2022)
- [Customized Segment Anything Model for Medical Image Segmentation](https://mlsanity.com/paper/2304.13785/) (2023)
- [Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video](https://mlsanity.com/paper/1908.10553/) (2019)
- [Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown Cameras](https://mlsanity.com/paper/1904.04998/) (2019)
- [LocalBins: Improving Depth Estimation by Learning Local Distributions](https://mlsanity.com/paper/2203.15132/) (2022)
- [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)
- [DeFeat-Net: General Monocular Depth via Simultaneous Unsupervised Representation Learning](https://mlsanity.com/paper/2003.13446/) (2020)
- [ViTMatte: Boosting Image Matting with Pretrained Plain Vision Transformers](https://mlsanity.com/paper/2305.15272/) (2023)
- [SC-DepthV3: Robust Self-supervised Monocular Depth Estimation for Dynamic Scenes](https://mlsanity.com/paper/2211.03660/) (2022)
- [Surgical-DINO: Adapter Learning of Foundation Models for Depth Estimation in Endoscopic Surgery](https://mlsanity.com/paper/2401.06013/) (2024)
- [Self-Prompting Large Vision Models for Few-Shot Medical Image Segmentation](https://mlsanity.com/paper/2308.07624/) (2023)
- [SAM Meets Robotic Surgery: An Empirical Study in Robustness Perspective](https://mlsanity.com/paper/2304.14674/) (2023)

## Built on by (most cited first, 30 of 44)

- [Endo3R: Unified Online Reconstruction from Dynamic Monocular Endoscopic Video](https://mlsanity.com/paper/2504.03198/) (2025)
- [EndoOmni: Zero-Shot Cross-Dataset Depth Estimation in Endoscopy by Robust Self-Learning from Noisy Labels](https://mlsanity.com/paper/2409.05442/) (2024)
- [Learning to Adapt Foundation Model DINOv2 for Capsule Endoscopy Diagnosis](https://mlsanity.com/paper/2406.10508/) (2024)
- [A Review of 3D Reconstruction Techniques for Deformable Tissues in Robotic Surgery](https://mlsanity.com/paper/2408.04426/) (2024)
- [SurgTPGS: Semantic 3D Surgical Scene Understanding with Text Promptable Gaussian Splatting](https://mlsanity.com/paper/2506.23309/) (2025)
- [Endo-FASt3r: Endoscopic Foundation model Adaptation for Structure from motion](https://mlsanity.com/paper/2503.07204/) (2025)
- [Advancing Depth Anything Model for Unsupervised Monocular Depth Estimation in Endoscopy](https://mlsanity.com/paper/2409.07723/) (2024)
- [NFL-BA: Near-Field Light Bundle Adjustment for SLAM in Dynamic Lighting](https://mlsanity.com/paper/2412.13176/) (2024)
- [ColonCrafter: A Depth Estimation Model for Colonoscopy Videos Using Diffusion Priors](https://mlsanity.com/paper/2509.13525/) (2025)
- [Zero-shot Monocular Metric Depth for Endoscopic Images](https://mlsanity.com/paper/2509.18642/) (2025)
- [ColonAdapter: Geometry Estimation Through Foundation Model Adaptation for Colonoscopy](https://mlsanity.com/paper/2511.22250/) (2025)
- [EndoStreamDepth: Temporally Consistent Monocular Depth Estimation for Endoscopic Video Streams](https://mlsanity.com/paper/2512.18159/) (2025)
- [RealSynCol: a high-fidelity synthetic colon dataset for 3D reconstruction applications](https://mlsanity.com/paper/2602.08397/) (2026)
- [EndoPerfect: High-Accuracy Monocular Depth Estimation and 3D Reconstruction for Endoscopic Surgery via NeRF-Stereo Fusion](https://mlsanity.com/paper/2410.04041/) (2024)
- [Learning to Efficiently Adapt Foundation Models for Self-Supervised Endoscopic 3D Scene Reconstruction from Any Cameras](https://mlsanity.com/paper/2503.15917/) (2025)
- [TR2M: Transferring Monocular Relative Depth to Metric Depth with Language Descriptions and Dual-Level Scale-Oriented Contrast](https://mlsanity.com/paper/2506.13387/) (2025)
- [Geo-RepNet: Geometry-Aware Representation Learning for Surgical Phase Recognition in Endoscopic Submucosal Dissection](https://mlsanity.com/paper/2507.09294/) (2025)
- [Endoscopic Depth Estimation Based on Deep Learning: A Survey](https://mlsanity.com/paper/2507.20881/) (2025)
- [EndoUFM: Utilizing Foundation Models for Monocular depth estimation of endoscopic images](https://mlsanity.com/paper/2508.17916/) (2025)
- [BREA-Depth: Bronchoscopy Realistic Airway-geometric Depth Estimation](https://mlsanity.com/paper/2509.11885/) (2025)
- [Moving Light Adaptive Colonoscopy Reconstruction via Illumination-Attenuation-Aware 3D Gaussian Splatting](https://mlsanity.com/paper/2510.18739/) (2025)
- [More than Segmentation: Benchmarking SAM 3 for Segmentation, 3D Perception, and Reconstruction in Robotic Surgery](https://mlsanity.com/paper/2512.07596/) (2025)
- [Head Pose Estimation and 3D Neural Surface Reconstruction via Monocular Camera in situ for Navigation and Safe Insertion into Natural Openings](https://mlsanity.com/paper/2406.13048/) (2024)
- [Towards Full-parameter and Parameter-efficient Self-learning For Endoscopic Camera Depth Estimation](https://mlsanity.com/paper/2410.00979/) (2024)
- [Self-supervised Monocular Depth and Pose Estimation for Endoscopy with Latent Priors](https://mlsanity.com/paper/2411.17790/) (2024)
- [V2-SfMLearner: Learning Monocular Depth and Ego-motion for Multimodal Wireless Capsule Endoscopy](https://mlsanity.com/paper/2412.17595/) (2024)
- [Efficient Self-Supervised Adaptation for Medical Image Analysis](https://mlsanity.com/paper/2503.18873/) (2025)
- [EndoMUST: Monocular Depth Estimation for Robotic Endoscopy via End-to-end Multi-step Self-supervised Training](https://mlsanity.com/paper/2506.16017/) (2025)
- [EndoGMDE: Generalizable Monocular Depth Estimation with Mixture of Low-Rank Experts for Diverse Endoscopic Scenes](https://mlsanity.com/paper/2509.01206/) (2025)
- [EndoSfM3D: Learning to 3D Reconstruct Any Endoscopic Surgery Scene using Self-supervised Foundation Model](https://mlsanity.com/paper/2510.22359/) (2025)

## Abstract (arXiv)

Depth estimation plays a crucial role in various tasks within endoscopic surgery, including navigation, surface reconstruction, and augmented reality visualization. Despite the significant achievements of foundation models in vision tasks, including depth estimation, their direct application to the medical domain often results in suboptimal performance. This highlights the need for efficient adaptation methods to adapt these models to endoscopic depth estimation. We propose Endoscopic Depth Any Camera (EndoDAC) which is an efficient self-supervised depth estimation framework that adapts foundation models to endoscopic scenes. Specifically, we develop the Dynamic Vector-Based Low-Rank Adaptation (DV-LoRA) and employ Convolutional Neck blocks to tailor the foundational model to the surgical domain, utilizing remarkably few trainable parameters. Given that camera information is not always accessible, we also introduce a self-supervised adaptation strategy that estimates camera intrinsics using the pose encoder. Our framework is capable of being trained solely on monocular surgical videos from any camera, ensuring minimal training costs. Experiments demonstrate that our approach obtains superior performance even with fewer training epochs and unaware of the ground truth camera intrinsics. Code is available at https://github.com/BeileiCui/EndoDAC.