# A geometry-aware deep network for depth estimation in monocular endoscopy arXiv: https://arxiv.org/abs/2304.10241 ยท HTML: https://mlsanity.com/paper/2304.10241/ ## Key facts - Cited by 8 papers in the map (data through 14 Oct 2026). - Builds on 27 of its 53 references that are in the map. - First posted on arXiv 20 Apr 2023. ## Builds on (most cited first, 27 of 27) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Depth Map Prediction from a Single Image using a Multi-Scale Deep Network](https://mlsanity.com/paper/1406.2283/) (2014) - [Unsupervised Monocular Depth Estimation with Left-Right Consistency](https://mlsanity.com/paper/1609.03677/) (2016) - [Accelerating 3D Deep Learning with PyTorch3D](https://mlsanity.com/paper/2007.08501/) (2020) - [Unsupervised Learning of Depth and Ego-Motion from Video](https://mlsanity.com/paper/1704.07813/) (2017) - [Deeper Depth Prediction with Fully Convolutional Residual Networks](https://mlsanity.com/paper/1606.00373/) (2016) - [From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation](https://mlsanity.com/paper/1907.10326/) (2019) - [Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue](https://mlsanity.com/paper/1603.04992/) (2016) - [The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth](https://mlsanity.com/paper/2104.14540/) (2021) - [Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints](https://mlsanity.com/paper/1802.05522/) (2018) - [Revisiting Single Image Depth Estimation: Toward Higher Resolution Maps with Accurate Object Boundaries](https://mlsanity.com/paper/1803.08673/) (2018) - [Deep Convolutional Neural Fields for Depth Estimation from a Single Image](https://mlsanity.com/paper/1411.6387/) (2014) - [Estimating Depth from Monocular Images as Classification Using Deep Fully Convolutional Residual Networks](https://mlsanity.com/paper/1605.02305/) (2016) - [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) - [Dense Depth Estimation in Monocular Endoscopy with Self-supervised Learning Methods](https://mlsanity.com/paper/1902.07766/) (2019) - [DefSLAM: Tracking and Mapping of Deforming Scenes from Monocular Sequences](https://mlsanity.com/paper/1908.08918/) (2019) - [Deep Learning and Conditional Random Fields-based Depth Estimation and Topographical Reconstruction from Conventional Endoscopy](https://mlsanity.com/paper/1710.11216/) (2017) - [MIS-SLAM: Real-time Large Scale Dense Deformable SLAM System in Minimal Invasive Surgery Based on Heterogeneous Computing](https://mlsanity.com/paper/1803.02009/) (2018) - [SLAM Endoscopy enhanced by adversarial depth prediction](https://mlsanity.com/paper/1907.00283/) (2019) - [When the Sun Goes Down: Repairing Photometric Losses for All-Day Depth Estimation](https://mlsanity.com/paper/2206.13850/) (2022) - [Reconstructing Sinus Anatomy from Endoscopic Video -- Towards a Radiation-free Approach for Quantitative Longitudinal Assessment](https://mlsanity.com/paper/2003.08502/) (2020) - [Extremely Dense Point Correspondences using a Learned Feature Descriptor](https://mlsanity.com/paper/2003.00619/) (2020) - [Self-Supervised Depth Estimation in Laparoscopic Image using 3D Geometric Consistency](https://mlsanity.com/paper/2208.08407/) (2022) - [SLAM based Quasi Dense Reconstruction For Minimally Invasive Surgery Scenes](https://mlsanity.com/paper/1705.09107/) (2017) - [Jumping Manifolds: Geometry Aware Dense Non-Rigid Structure from Motion](https://mlsanity.com/paper/1902.01077/) (2019) - [A Non-Rigid Map Fusion-Based RGB-Depth SLAM Method for Endoscopic Capsule Robots](https://mlsanity.com/paper/1705.05444/) (2017) ## Built on by (most cited first, 8 of 8) - [Transferring Relative Monocular Depth to Surgical Vision with Temporal Consistency](https://mlsanity.com/paper/2403.06683/) (2024) - [Multi-task learning with cross-task consistency for improved depth estimation in colonoscopy](https://mlsanity.com/paper/2311.18664/) (2023) - [Geometric Constraints in Deep Learning Frameworks: A Survey](https://mlsanity.com/paper/2403.12431/) (2024) - [Generalizing monocular colonoscopy image depth estimation by uncertainty-based global and local fusion network](https://mlsanity.com/paper/2409.15006/) (2024) - [Endoscopic Depth Estimation Based on Deep Learning: A Survey](https://mlsanity.com/paper/2507.20881/) (2025) - [Learning with Geometric Priors in U-Net Variants for Polyp Segmentation](https://mlsanity.com/paper/2601.17331/) (2026) - [Attention-Aware Laparoscopic Image Desmoking Network with Lightness Embedding and Hybrid Guided Embedding](https://mlsanity.com/paper/2404.07556/) (2024) - [Multi-Modal Monocular Endoscopic Depth and Pose Estimation with Edge-Guided Self-Supervision](https://mlsanity.com/paper/2602.17785/) (2026) ## Abstract (arXiv) Monocular depth estimation is critical for endoscopists to perform spatial perception and 3D navigation of surgical sites. However, most of the existing methods ignore the important geometric structural consistency, which inevitably leads to performance degradation and distortion of 3D reconstruction. To address this issue, we introduce a gradient loss to penalize edge fluctuations ambiguous around stepped edge structures and a normal loss to explicitly express the sensitivity to frequently small structures, and propose a geometric consistency loss to spreads the spatial information across the sample grids to constrain the global geometric anatomy structures. In addition, we develop a synthetic RGB-Depth dataset that captures the anatomical structures under reflections and illumination variations. The proposed method is extensively validated across different datasets and clinical images and achieves mean RMSE values of 0.066 (stomach), 0.029 (small intestine), and 0.139 (colon) on the EndoSLAM dataset. The generalizability of the proposed method achieves mean RMSE values of 12.604 (T1-L1), 9.930 (T2-L2), and 13.893 (T3-L3) on the ColonDepth dataset. The experimental results show that our method exceeds previous state-of-the-art competitors and generates more consistent depth maps and reasonable anatomical structures. The quality of intraoperative 3D structure perception from endoscopic videos of the proposed method meets the accuracy requirements of video-CT registration algorithms for endoscopic navigation. The dataset and the source code will be available at https://github.com/YYM-SIA/LINGMI-MR.