# Self-Supervised Depth Estimation in Laparoscopic Image using 3D Geometric Consistency

arXiv: https://arxiv.org/abs/2208.08407 · HTML: https://mlsanity.com/paper/2208.08407/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 25 references that are in the map.
- First posted on arXiv 17 Aug 2022.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Unsupervised Monocular Depth Estimation with Left-Right Consistency](https://mlsanity.com/paper/1609.03677/) (2016)
- [3D Packing for Self-Supervised Monocular Depth Estimation](https://mlsanity.com/paper/1905.02693/) (2019)
- [RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching](https://mlsanity.com/paper/2109.07547/) (2021)
- [Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video](https://mlsanity.com/paper/1908.10553/) (2019)
- [Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints](https://mlsanity.com/paper/1802.05522/) (2018)
- [Self-Supervised Monocular Depth Estimation: Solving the Dynamic Object Problem by Semantic Guidance](https://mlsanity.com/paper/2007.06936/) (2020)
- [Self-supervised Monocular Trained Depth Estimation using Self-attention and Discrete Disparity Volume](https://mlsanity.com/paper/2003.13951/) (2020)
- [Stereo Correspondence and Reconstruction of Endoscopic Data Challenge](https://mlsanity.com/paper/2101.01133/) (2021)
- [Fine-grained Semantics-aware Representation Enhancement for Self-supervised Monocular Depth Estimation](https://mlsanity.com/paper/2108.08829/) (2021)
- [Dense Depth Estimation in Monocular Endoscopy with Self-supervised Learning Methods](https://mlsanity.com/paper/1902.07766/) (2019)
- [Self-Supervised Generative Adversarial Network for Depth Estimation in Laparoscopic Images](https://mlsanity.com/paper/2107.04644/) (2021)
- [H-Net: Unsupervised Attention-based Stereo Depth Estimation Leveraging Epipolar Geometry](https://mlsanity.com/paper/2104.11288/) (2021)
- [Light-weight Deformable Registration using Adversarial Learning with Distilling Knowledge](https://mlsanity.com/paper/2110.01293/) (2021)

## Built on by (most cited first, 19 of 19)

- [C3VDv2 -- Colonoscopy 3D video dataset with enhanced realism](https://mlsanity.com/paper/2506.24074/) (2025)
- [SurgicalGS: Dynamic 3D Gaussian Splatting for Accurate Robotic-Assisted Surgical Scene Reconstruction](https://mlsanity.com/paper/2410.09292/) (2024)
- [MonoPCC: Photometric-invariant Cycle Constraint for Monocular Depth Estimation of Endoscopic Images](https://mlsanity.com/paper/2404.16571/) (2024)
- [A geometry-aware deep network for depth estimation in monocular endoscopy](https://mlsanity.com/paper/2304.10241/) (2023)
- [CathAction: A Benchmark for Endovascular Intervention Understanding](https://mlsanity.com/paper/2408.13126/) (2024)
- [Detecting the Sensing Area of A Laparoscopic Probe in Minimally Invasive Cancer Surgery](https://mlsanity.com/paper/2307.03662/) (2023)
- [Multitask Learning in Minimally Invasive Surgical Vision: A Review](https://mlsanity.com/paper/2401.08256/) (2024)
- [Tracking Everything in Robotic-Assisted Surgery](https://mlsanity.com/paper/2409.19821/) (2024)
- [Shape-Sensitive Loss for Catheter and Guidewire Segmentation](https://mlsanity.com/paper/2311.11205/) (2023)
- [SemHint-MD: Learning from Noisy Semantic Labels for Self-Supervised Monocular Depth Estimation](https://mlsanity.com/paper/2303.18219/) (2023)
- [3D Guidewire Shape Reconstruction from Monoplane Fluoroscopic Images](https://mlsanity.com/paper/2311.11209/) (2023)
- [High-fidelity Endoscopic Image Synthesis by Utilizing Depth-guided Neural Surfaces](https://mlsanity.com/paper/2404.13437/) (2024)
- [A Compacted Structure for Cross-domain learning on Monocular Depth and Flow Estimation](https://mlsanity.com/paper/2208.11993/) (2022)
- [Towards Abdominal 3-D Scene Rendering from Laparoscopy Surgical Videos using NeRFs](https://mlsanity.com/paper/2310.11645/) (2023)
- [Guide3D: A Bi-planar X-ray Dataset for 3D Shape Reconstruction](https://mlsanity.com/paper/2410.22224/) (2024)
- [Nested ResNet: A Vision-Based Method for Detecting the Sensing Area of a Drop-in Gamma Probe](https://mlsanity.com/paper/2410.23154/) (2024)
- [SplineFormer: An Explainable Transformer-Based Approach for Autonomous Endovascular Navigation](https://mlsanity.com/paper/2501.04515/) (2025)
- [Optical Computation-in-Communication enables low-latency, high-fidelity perception in telesurgery](https://mlsanity.com/paper/2510.14058/) (2025)
- [DeLightMono: Enhancing Self-Supervised Monocular Depth Estimation in Endoscopy by Decoupling Uneven Illumination](https://mlsanity.com/paper/2511.20058/) (2025)

## Abstract (arXiv)

Depth estimation is a crucial step for image-guided intervention in robotic surgery and laparoscopic imaging system. Since per-pixel depth ground truth is difficult to acquire for laparoscopic image data, it is rarely possible to apply supervised depth estimation to surgical applications. As an alternative, self-supervised methods have been introduced to train depth estimators using only synchronized stereo image pairs. However, most recent work focused on the left-right consistency in 2D and ignored valuable inherent 3D information on the object in real world coordinates, meaning that the left-right 3D geometric structural consistency is not fully utilized. To overcome this limitation, we present M3Depth, a self-supervised depth estimator to leverage 3D geometric structural information hidden in stereo pairs while keeping monocular inference. The method also removes the influence of border regions unseen in at least one of the stereo images via masking, to enhance the correspondences between left and right images in overlapping areas. Intensive experiments show that our method outperforms previous self-supervised approaches on both a public dataset and a newly acquired dataset by a large margin, indicating a good generalization across different samples and laparoscopes. Code and data are available at https://github.com/br0202/M3Depth.