# C3Fusion: Consistent Contrastive Colon Fusion, Towards Deep SLAM in Colonoscopy

arXiv: https://arxiv.org/abs/2206.01961 · HTML: https://mlsanity.com/paper/2206.01961/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 47 references that are in the map.
- First posted on arXiv 4 Jun 2022.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Deformable Convolutional Networks](https://mlsanity.com/paper/1703.06211/) (2017)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [Unsupervised Learning of Depth and Ego-Motion from Video](https://mlsanity.com/paper/1704.07813/) (2017)
- [Direct Sparse Odometry](https://mlsanity.com/paper/1607.02565/) (2016)
- [Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video](https://mlsanity.com/paper/1908.10553/) (2019)
- [Self-Supervised Monocular Depth and Ego-Motion Estimation in Endoscopy: Appearance Flow to the Rescue](https://mlsanity.com/paper/2112.08122/) (2021)
- [FisheyeDistanceNet: Self-Supervised Scale-Aware Distance Estimation using Monocular Fisheye Camera for Autonomous Driving](https://mlsanity.com/paper/1910.04076/) (2019)
- [Deep Keypoint-Based Camera Pose Estimation with Geometric Constraints](https://mlsanity.com/paper/2007.15122/) (2020)
- [SLAM Endoscopy enhanced by adversarial depth prediction](https://mlsanity.com/paper/1907.00283/) (2019)
- [Distortion-aware Monocular Depth Estimation for Omnidirectional Images](https://mlsanity.com/paper/2010.08942/) (2020)
- [Motion-based Camera Localization System in Colonoscopy Videos](https://mlsanity.com/paper/2012.01690/) (2020)
- [Lighting Enhancement Aids Reconstruction of Colonoscopic Surfaces](https://mlsanity.com/paper/2103.10310/) (2021)
- [ColDE: A Depth Estimation Framework for Colonoscopy Reconstruction](https://mlsanity.com/paper/2111.10371/) (2021)
- [Learning-Based Depth and Pose Estimation for Monocular Endoscope with Loss Generalization](https://mlsanity.com/paper/2107.13263/) (2021)

## Built on by (most cited first, 7 of 7)

- [EndoGSLAM: Real-Time Dense Reconstruction and Tracking in Endoscopic Surgeries using Gaussian Splatting](https://mlsanity.com/paper/2403.15124/) (2024)
- [Advancing Dense Endoscopic Reconstruction with Gaussian Splatting-driven Surface Normal-aware Tracking and Mapping](https://mlsanity.com/paper/2501.19319/) (2025)
- [ToDER: Towards Colonoscopy Depth Estimation and Reconstruction with Geometry Constraint Adaptation](https://mlsanity.com/paper/2407.16508/) (2024)
- [ColNav: Real-Time Colon Navigation for Colonoscopy](https://mlsanity.com/paper/2306.04269/) (2023)
- [Endoscopic Depth Estimation Based on Deep Learning: A Survey](https://mlsanity.com/paper/2507.20881/) (2025)
- [Estimating the coverage in 3d reconstructions of the colon from colonoscopy videos](https://mlsanity.com/paper/2210.10459/) (2022)
- [Colonoscopy Coverage Revisited: Identifying Scanning Gaps in Real-Time](https://mlsanity.com/paper/2305.10026/) (2023)

## Abstract (arXiv)

3D colon reconstruction from Optical Colonoscopy (OC) to detect non-examined surfaces remains an unsolved problem. The challenges arise from the nature of optical colonoscopy data, characterized by highly reflective low-texture surfaces, drastic illumination changes and frequent tracking loss. Recent methods demonstrate compelling results, but suffer from: (1) frangible frame-to-frame (or frame-to-model) pose estimation resulting in many tracking failures; or (2) rely on point-based representations at the cost of scan quality. In this paper, we propose a novel reconstruction framework that addresses these issues end to end, which result in both quantitatively and qualitatively accurate and robust 3D colon reconstruction. Our SLAM approach, which employs correspondences based on contrastive deep features, and deep consistent depth maps, estimates globally optimized poses, is able to recover from frequent tracking failures, and estimates a global consistent 3D model; all within a single framework. We perform an extensive experimental evaluation on multiple synthetic and real colonoscopy videos, showing high-quality results and comparisons against relevant baselines.