# SLAM Endoscopy enhanced by adversarial depth prediction

arXiv: https://arxiv.org/abs/1907.00283 · HTML: https://mlsanity.com/paper/1907.00283/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2019.

## Built on by (most cited first, 26 of 26)

- [Endo-Depth-and-Motion: Reconstruction and Tracking in Endoscopic Videos using Depth Networks and Photometric Constraints](https://mlsanity.com/paper/2103.16525/) (2021)
- [Colonoscopy 3D Video Dataset with Paired Depth from 2D-3D Registration](https://mlsanity.com/paper/2206.08903/) (2022)
- [Self-Supervised Monocular Depth and Ego-Motion Estimation in Endoscopy: Appearance Flow to the Rescue](https://mlsanity.com/paper/2112.08122/) (2021)
- [LightDepth: Single-View Depth Self-Supervision from Illumination Decline](https://mlsanity.com/paper/2308.10525/) (2023)
- [C3VDv2 -- Colonoscopy 3D video dataset with enhanced realism](https://mlsanity.com/paper/2506.24074/) (2025)
- [Adversarial Domain Feature Adaptation for Bronchoscopic Depth Estimation](https://mlsanity.com/paper/2109.11798/) (2021)
- [Depth Anything in Medical Images: A Comparative Study](https://mlsanity.com/paper/2401.16600/) (2024)
- [A geometry-aware deep network for depth estimation in monocular endoscopy](https://mlsanity.com/paper/2304.10241/) (2023)
- [C3Fusion: Consistent Contrastive Colon Fusion, Towards Deep SLAM in Colonoscopy](https://mlsanity.com/paper/2206.01961/) (2022)
- [On the Uncertain Single-View Depths in Colonoscopies](https://mlsanity.com/paper/2112.08906/) (2021)
- [FoldIt: Haustral Folds Detection and Segmentation in Colonoscopy Videos](https://mlsanity.com/paper/2106.12522/) (2021)
- [Learning-Based Depth and Pose Estimation for Monocular Endoscope with Loss Generalization](https://mlsanity.com/paper/2107.13263/) (2021)
- [Semi-supervised Bladder Tissue Classification in Multi-Domain Endoscopic Images](https://mlsanity.com/paper/2212.11375/) (2022)
- [BodySLAM: A Generalized Monocular Visual SLAM Framework for Surgical Applications](https://mlsanity.com/paper/2408.03078/) (2024)
- [A comprehensive survey on recent deep learning-based methods applied to surgical data](https://mlsanity.com/paper/2209.01435/) (2022)
- [ColonCrafter: A Depth Estimation Model for Colonoscopy Videos Using Diffusion Priors](https://mlsanity.com/paper/2509.13525/) (2025)
- [A Quantitative Evaluation of Dense 3D Reconstruction of Sinus Anatomy from Monocular Endoscopic Video](https://mlsanity.com/paper/2310.14364/) (2023)
- [ColonNeRF: High-Fidelity Neural Reconstruction of Long Colonoscopy](https://mlsanity.com/paper/2312.02015/) (2023)
- [ToDER: Towards Colonoscopy Depth Estimation and Reconstruction with Geometry Constraint Adaptation](https://mlsanity.com/paper/2407.16508/) (2024)
- [RealSynCol: a high-fidelity synthetic colon dataset for 3D reconstruction applications](https://mlsanity.com/paper/2602.08397/) (2026)
- [3D Semantic Mapping from Arthroscopy using Out-of-distribution Pose and Depth and In-distribution Segmentation Training](https://mlsanity.com/paper/2106.05525/) (2021)
- [3 Dimensional Dense Reconstruction: A Review of Algorithms and Dataset](https://mlsanity.com/paper/2304.09371/) (2023)
- [Colonoscopy Coverage Revisited: Identifying Scanning Gaps in Real-Time](https://mlsanity.com/paper/2305.10026/) (2023)
- [Uncertainty and Self-Supervision in Single-View Depth](https://mlsanity.com/paper/2406.14226/) (2024)
- [EndoMUST: Monocular Depth Estimation for Robotic Endoscopy via End-to-end Multi-step Self-supervised Training](https://mlsanity.com/paper/2506.16017/) (2025)
- [ST-ColoNet: Spatio-Temporal Colon Segment Recognition via Hybrid Attention and Edge-Guided Feature Learning](https://mlsanity.com/paper/2605.28119/) (2026)

## Abstract (arXiv)

Medical endoscopy remains a challenging application for simultaneous localization and mapping (SLAM) due to the sparsity of image features and size constraints that prevent direct depth-sensing. We present a SLAM approach that incorporates depth predictions made by an adversarially-trained convolutional neural network (CNN) applied to monocular endoscopy images. The depth network is trained with synthetic images of a simple colon model, and then fine-tuned with domain-randomized, photorealistic images rendered from computed tomography measurements of human colons. Each image is paired with an error-free depth map for supervised adversarial learning. Monocular RGB images are then fused with corresponding depth predictions, enabling dense reconstruction and mosaicing as an endoscope is advanced through the gastrointestinal tract. Our preliminary results demonstrate that incorporating monocular depth estimation into a SLAM architecture can enable dense reconstruction of endoscopic scenes.