# C3VDv2 -- Colonoscopy 3D video dataset with enhanced realism

arXiv: https://arxiv.org/abs/2506.24074 · HTML: https://mlsanity.com/paper/2506.24074/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 69 references that are in the map.
- First posted on arXiv 30 Jun 2025.

## Builds on (most cited first, 28 of 28)

- [Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection](https://mlsanity.com/paper/1909.01955/) (2019)
- [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)
- [EndoMapper dataset of complete calibrated endoscopy procedures](https://mlsanity.com/paper/2204.14240/) (2022)
- [EndoGSLAM: Real-Time Dense Reconstruction and Tracking in Endoscopic Surgeries using Gaussian Splatting](https://mlsanity.com/paper/2403.15124/) (2024)
- [Deep Learning and Conditional Random Fields-based Depth Estimation and Topographical Reconstruction from Conventional Endoscopy](https://mlsanity.com/paper/1710.11216/) (2017)
- [SLAM Endoscopy enhanced by adversarial depth prediction](https://mlsanity.com/paper/1907.00283/) (2019)
- [A deep learning framework for quality assessment and restoration in video endoscopy](https://mlsanity.com/paper/1904.07073/) (2019)
- [LightNeuS: Neural Surface Reconstruction in Endoscopy using Illumination Decline](https://mlsanity.com/paper/2309.02777/) (2023)
- [Bimodal Camera Pose Prediction for Endoscopy](https://mlsanity.com/paper/2204.04968/) (2022)
- [Self-Supervised Depth Estimation in Laparoscopic Image using 3D Geometric Consistency](https://mlsanity.com/paper/2208.08407/) (2022)
- [SimCol3D -- 3D Reconstruction during Colonoscopy Challenge](https://mlsanity.com/paper/2307.11261/) (2023)
- [Gaussian Pancakes: Geometrically-Regularized 3D Gaussian Splatting for Realistic Endoscopic Reconstruction](https://mlsanity.com/paper/2404.06128/) (2024)
- [Leveraging Near-Field Lighting for Monocular Depth Estimation from Endoscopy Videos](https://mlsanity.com/paper/2403.17915/) (2024)
- [EndoOmni: Zero-Shot Cross-Dataset Depth Estimation in Endoscopy by Robust Self-Learning from Noisy Labels](https://mlsanity.com/paper/2409.05442/) (2024)
- [Photometric single-view dense 3D reconstruction in endoscopy](https://mlsanity.com/paper/2204.09083/) (2022)
- [CudaSIFT-SLAM: multiple-map visual SLAM for full procedure mapping in real human endoscopy](https://mlsanity.com/paper/2405.16932/) (2024)
- [Deep Learning with Cinematic Rendering: Fine-Tuning Deep Neural Networks Using Photorealistic Medical Images](https://mlsanity.com/paper/1805.08400/) (2018)
- [Tracking monocular camera pose and deformation for SLAM inside the human body](https://mlsanity.com/paper/2204.08309/) (2022)
- [A Surface-normal Based Neural Framework for Colonoscopy Reconstruction](https://mlsanity.com/paper/2303.07264/) (2023)
- [Multi-task learning with cross-task consistency for improved depth estimation in colonoscopy](https://mlsanity.com/paper/2311.18664/) (2023)
- [GAN Inversion for Data Augmentation to Improve Colonoscopy Lesion Classification](https://mlsanity.com/paper/2205.02840/) (2022)
- [Improving colonoscopy lesion classification using semi-supervised deep learning](https://mlsanity.com/paper/2009.03162/) (2020)
- [Multimodal Densenet](https://mlsanity.com/paper/1811.07407/) (2018)
- [GANPOP: Generative Adversarial Network Prediction of Optical Properties from Single Snapshot Wide-field Images](https://mlsanity.com/paper/1906.05360/) (2019)
- [EndoMetric: Near-Light Monocular Metric Scale Estimation in Endoscopy](https://mlsanity.com/paper/2410.15065/) (2024)
- [Diverse Image Generation with Diffusion Models and Cross Class Label Learning for Polyp Classification](https://mlsanity.com/paper/2502.05444/) (2025)
- [High-fidelity Endoscopic Image Synthesis by Utilizing Depth-guided Neural Surfaces](https://mlsanity.com/paper/2404.13437/) (2024)

## Built on by (most cited first, 15 of 15)

- [ColonCrafter: A Depth Estimation Model for Colonoscopy Videos Using Diffusion Priors](https://mlsanity.com/paper/2509.13525/) (2025)
- [BREATH-VL: Vision-Language-Guided 6-DoF Bronchoscopy Localization via Semantic-Geometric Fusion](https://mlsanity.com/paper/2601.03713/) (2026)
- [NRGS-SLAM: Monocular Non-Rigid SLAM for Endoscopy via Deformation-Aware 3D Gaussian Splatting](https://mlsanity.com/paper/2602.17182/) (2026)
- [ColonSplat: Reconstruction of Peristaltic Motion in Colonoscopy with Dynamic Gaussian Splatting](https://mlsanity.com/paper/2603.06860/) (2026)
- [Endoscopic Depth Estimation Based on Deep Learning: A Survey](https://mlsanity.com/paper/2507.20881/) (2025)
- [DepthPilot: From Controllability to Interpretability in Colonoscopy Video Generation](https://mlsanity.com/paper/2604.26232/) (2026)
- [SCARED-C: Corrected Camera Poses for Endoscopic Depth Estimation](https://mlsanity.com/paper/2605.16628/) (2026)
- [CoGE: Sim-to-Real Online Geometric Estimation for Monocular Colonoscopy](https://mlsanity.com/paper/2605.13038/) (2026)
- [C3VD-DEFCOL: A Deformable Colonoscopy Dataset with Time-Resolved 3D Ground Truth and Realistic Appearance](https://mlsanity.com/paper/2606.07891/) (2026)
- [Gaussian Process Prior Variational Autoencoder for Endoscopic Videos](https://mlsanity.com/paper/2606.19908/) (2026)
- [ExtraGS: Enhancing Endoscopic View Extrapolation via Diffusion-Guided 3D Gaussian Splatting](https://mlsanity.com/paper/2607.12785/) (2026)
- [EndoMD-SLAM: Endoscopic Gaussian Splatting SLAM under Optical Degradation with Memory and Static-Transient Decomposition](https://mlsanity.com/paper/2608.08949/) (2026)
- [DART: Depth-as-Target Pretraining for Surgical Vision Foundation Models](https://mlsanity.com/paper/2609.04555/) (2026)
- [Lesion-centered 3D mapping of colonoscopy procedures: validation of a hierarchical ensemble pipeline on public benchmark videos](https://mlsanity.com/paper/2609.16672/) (2026)
- [SCOPE-4D: Endoscopic 4D Geometry Foundation Models](https://mlsanity.com/paper/2610.02343/) (2026)

## Abstract (arXiv)

Spatial computer vision techniques have the potential to improve the diagnostic performance of colonoscopy. However, the lack of 3D colonoscopy datasets for training and validation hinders their development. This paper introduces C3VDv2, the second version (v2) of the high-definition Colonoscopy 3D Video Dataset, featuring enhanced realism designed to facilitate the quantitative evaluation of 3D colon reconstruction algorithms. 192 video sequences totaling 169,371 frames were captured by imaging 60 unique, high-fidelity silicone colon phantom segments. Ground truth depth, surface normals, optical flow, occlusion, diffuse maps, six-degree-of-freedom pose, coverage map, and 3D models are provided for 169 colonoscopy videos. Eight simulated screening colonoscopy videos acquired by a gastroenterologist are provided with ground truth poses. Lastly, the dataset includes 15 videos with colon deformations for qualitative assessment. C3VDv2 emulates diverse and challenging scenarios for 3D reconstruction algorithms, including fecal debris, mucous pools, blood, debris obscuring the colonoscope lens, en-face views, and fast camera motion. The enhanced realism of C3VDv2 will allow for more robust and representative development and evaluation of 3D reconstruction algorithms. Project Page - https://durrlab.github.io/C3VDv2/