# Task-guided Domain Gap Reduction for Monocular Depth Prediction in Endoscopy

arXiv: https://arxiv.org/abs/2310.01663 · HTML: https://mlsanity.com/paper/2310.01663/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 19 references that are in the map.
- First posted on arXiv 2 Oct 2023.

## Builds on (most cited first, 10 of 10)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Self-Supervised Monocular Depth and Ego-Motion Estimation in Endoscopy: Appearance Flow to the Rescue](https://mlsanity.com/paper/2112.08122/) (2021)
- [T2Net: Synthetic-to-Realistic Translation for Solving Single-Image Depth Estimation Tasks](https://mlsanity.com/paper/1808.01454/) (2018)
- [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)
- [Augmenting Colonoscopy using Extended and Directional CycleGAN for Lossy Image Translation](https://mlsanity.com/paper/2003.12473/) (2020)
- [SharinGAN: Combining Synthetic and Real Data for Unsupervised Geometry Estimation](https://mlsanity.com/paper/2006.04026/) (2020)
- [Bimodal Camera Pose Prediction for Endoscopy](https://mlsanity.com/paper/2204.04968/) (2022)
- [Unsupervised Odometry and Depth Learning for Endoscopic Capsule Robots](https://mlsanity.com/paper/1803.01047/) (2018)
- [Detecting Deficient Coverage in Colonoscopies](https://mlsanity.com/paper/2001.08589/) (2020)

## Built on by (most cited first, 5 of 5)

- [Hard-Attention Gates with Gradient Routing for Endoscopic Image Computing](https://mlsanity.com/paper/2407.04400/) (2024)
- [SHADeS: Self-supervised Monocular Depth Estimation Through Non-Lambertian Image Decomposition](https://mlsanity.com/paper/2502.12994/) (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)
- [Multi-Modal Monocular Endoscopic Depth and Pose Estimation with Edge-Guided Self-Supervision](https://mlsanity.com/paper/2602.17785/) (2026)

## Abstract (arXiv)

Colorectal cancer remains one of the deadliest cancers in the world. In recent years computer-aided methods have aimed to enhance cancer screening and improve the quality and availability of colonoscopies by automatizing sub-tasks. One such task is predicting depth from monocular video frames, which can assist endoscopic navigation. As ground truth depth from standard in-vivo colonoscopy remains unobtainable due to hardware constraints, two approaches have aimed to circumvent the need for real training data: supervised methods trained on labeled synthetic data and self-supervised models trained on unlabeled real data. However, self-supervised methods depend on unreliable loss functions that struggle with edges, self-occlusion, and lighting inconsistency. Methods trained on synthetic data can provide accurate depth for synthetic geometries but do not use any geometric supervisory signal from real data and overfit to synthetic anatomies and properties. This work proposes a novel approach to leverage labeled synthetic and unlabeled real data. While previous domain adaptation methods indiscriminately enforce the distributions of both input data modalities to coincide, we focus on the end task, depth prediction, and translate only essential information between the input domains. Our approach results in more resilient and accurate depth maps of real colonoscopy sequences.