mlsanityData to 14 Oct 2026

A geometry-aware deep network for depth estimation in monocular endoscopy

arXiv 2304.10241 ↗First posted 20 Apr 2023cs.CVcited by 8

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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.

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

27 of 53 references.

References · 27

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Depth Map Prediction from a Single Image using a Multi-Scale Deep Networkcited by 1,274
  3. 2016Unsupervised Monocular Depth Estimation with Left-Right Consistencycited by 820
  4. 2020Accelerating 3D Deep Learning with PyTorch3Dcited by 784
  5. 2017Unsupervised Learning of Depth and Ego-Motion from Videocited by 782
  6. 2016Deeper Depth Prediction with Fully Convolutional Residual Networkscited by 403
  7. 2019From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimationcited by 382
  8. 2016Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescuecited by 368
  9. 2021The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depthcited by 190
  10. 2018Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraintscited by 179
  11. 2018Revisiting Single Image Depth Estimation: Toward Higher Resolution Maps with Accurate Object Boundariescited by 145
  12. 2014Deep Convolutional Neural Fields for Depth Estimation from a Single Imagecited by 106
15 more
  1. 2016Estimating Depth from Monocular Images as Classification Using Deep Fully Convolutional Residual Networkscited by 91
  2. 2021Endo-Depth-and-Motion: Reconstruction and Tracking in Endoscopic Videos using Depth Networks and Photometric Constraintscited by 81
  3. 2021Self-Supervised Monocular Depth and Ego-Motion Estimation in Endoscopy: Appearance Flow to the Rescuecited by 72
  4. 2019Dense Depth Estimation in Monocular Endoscopy with Self-supervised Learning Methodscited by 47
  5. 2019DefSLAM: Tracking and Mapping of Deforming Scenes from Monocular Sequencescited by 37
  6. 2017Deep Learning and Conditional Random Fields-based Depth Estimation and Topographical Reconstruction from Conventional Endoscopycited by 31
  7. 2018MIS-SLAM: Real-time Large Scale Dense Deformable SLAM System in Minimal Invasive Surgery Based on Heterogeneous Computingcited by 30
  8. 2019SLAM Endoscopy enhanced by adversarial depth predictioncited by 26
  9. 2022When the Sun Goes Down: Repairing Photometric Losses for All-Day Depth Estimationcited by 24
  10. 2020Reconstructing Sinus Anatomy from Endoscopic Video -- Towards a Radiation-free Approach for Quantitative Longitudinal Assessmentcited by 23
  11. 2020Extremely Dense Point Correspondences using a Learned Feature Descriptorcited by 20
  12. 2022Self-Supervised Depth Estimation in Laparoscopic Image using 3D Geometric Consistencycited by 19
  13. 2017SLAM based Quasi Dense Reconstruction For Minimally Invasive Surgery Scenescited by 12
  14. 2019Jumping Manifolds: Geometry Aware Dense Non-Rigid Structure from Motioncited by 12
  15. 2017A Non-Rigid Map Fusion-Based RGB-Depth SLAM Method for Endoscopic Capsule Robotscited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 4; 2025: 1; 2026: 2.

8 papers.

  1. 2024Transferring Relative Monocular Depth to Surgical Vision with Temporal Consistencycited by 13
  2. 2023Multi-task learning with cross-task consistency for improved depth estimation in colonoscopycited by 8
  3. 2024Geometric Constraints in Deep Learning Frameworks: A Surveycited by 1
  4. 2024Generalizing monocular colonoscopy image depth estimation by uncertainty-based global and local fusion networkcited by 1
  5. 2025Endoscopic Depth Estimation Based on Deep Learning: A Surveycited by 1
  6. 2026Learning with Geometric Priors in U-Net Variants for Polyp Segmentationcited by 1
  7. 2024Attention-Aware Laparoscopic Image Desmoking Network with Lightness Embedding and Hybrid Guided Embeddingcited by 0
  8. 2026Multi-Modal Monocular Endoscopic Depth and Pose Estimation with Edge-Guided Self-Supervisioncited by 0