# Structure-preserving Image Translation for Depth Estimation in Colonoscopy Video

arXiv: https://arxiv.org/abs/2408.10153 · HTML: https://mlsanity.com/paper/2408.10153/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 19 Aug 2024.

## Builds on (most cited first, 6 of 6)

- [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://mlsanity.com/paper/2401.10891/) (2024)
- [Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D Scans](https://mlsanity.com/paper/2110.04994/) (2021)
- [Colonoscopy 3D Video Dataset with Paired Depth from 2D-3D Registration](https://mlsanity.com/paper/2206.08903/) (2022)
- [EndoMapper dataset of complete calibrated endoscopy procedures](https://mlsanity.com/paper/2204.14240/) (2022)
- [Deep Learning and Conditional Random Fields-based Depth Estimation and Topographical Reconstruction from Conventional Endoscopy](https://mlsanity.com/paper/1710.11216/) (2017)
- [A Surface-normal Based Neural Framework for Colonoscopy Reconstruction](https://mlsanity.com/paper/2303.07264/) (2023)

## Built on by (most cited first, 6 of 6)

- [3D Densification for Multi-Map Monocular VSLAM in Endoscopy](https://mlsanity.com/paper/2503.14346/) (2025)
- [ColonCrafter: A Depth Estimation Model for Colonoscopy Videos Using Diffusion Priors](https://mlsanity.com/paper/2509.13525/) (2025)
- [PPS-Ctrl: Controllable Sim-to-Real Translation for Colonoscopy Depth Estimation](https://mlsanity.com/paper/2504.17067/) (2025)
- [Monocular absolute depth estimation from endoscopy via domain-invariant feature learning and latent consistency](https://mlsanity.com/paper/2511.02247/) (2025)
- [Endoscopic Depth Estimation Based on Deep Learning: A Survey](https://mlsanity.com/paper/2507.20881/) (2025)
- [Structure-to-Image: Zero-Shot Depth Estimation in Colonoscopy via High-Fidelity Sim-to-Real Adaptation](https://mlsanity.com/paper/2602.21740/) (2026)

## Abstract (arXiv)

Monocular depth estimation in colonoscopy video aims to overcome the unusual lighting properties of the colonoscopic environment. One of the major challenges in this area is the domain gap between annotated but unrealistic synthetic data and unannotated but realistic clinical data. Previous attempts to bridge this domain gap directly target the depth estimation task itself. We propose a general pipeline of structure-preserving synthetic-to-real (sim2real) image translation (producing a modified version of the input image) to retain depth geometry through the translation process. This allows us to generate large quantities of realistic-looking synthetic images for supervised depth estimation with improved generalization to the clinical domain. We also propose a dataset of hand-picked sequences from clinical colonoscopies to improve the image translation process. We demonstrate the simultaneous realism of the translated images and preservation of depth maps via the performance of downstream depth estimation on various datasets.