# RealSynCol: a high-fidelity synthetic colon dataset for 3D reconstruction applications

arXiv: https://arxiv.org/abs/2602.08397 · HTML: https://mlsanity.com/paper/2602.08397/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 45 references that are in the map.
- First posted on arXiv 9 Feb 2026.

## Builds on (most cited first, 15 of 15)

- [Depth Anything V2](https://mlsanity.com/paper/2406.09414/) (2024)
- [Unsupervised Learning of Depth and Ego-Motion from Video](https://mlsanity.com/paper/1704.07813/) (2017)
- [Kvasir-SEG: A Segmented Polyp Dataset](https://mlsanity.com/paper/1911.07069/) (2019)
- [MonoViT: Self-Supervised Monocular Depth Estimation with a Vision Transformer](https://mlsanity.com/paper/2208.03543/) (2022)
- [Lite-Mono: A Lightweight CNN and Transformer Architecture for Self-Supervised Monocular Depth Estimation](https://mlsanity.com/paper/2211.13202/) (2022)
- [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)
- [EndoMapper dataset of complete calibrated endoscopy procedures](https://mlsanity.com/paper/2204.14240/) (2022)
- [EndoDAC: Efficient Adapting Foundation Model for Self-Supervised Depth Estimation from Any Endoscopic Camera](https://mlsanity.com/paper/2405.08672/) (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)
- [VR-Caps: A Virtual Environment for Capsule Endoscopy](https://mlsanity.com/paper/2008.12949/) (2020)
- [SimCol3D -- 3D Reconstruction during Colonoscopy Challenge](https://mlsanity.com/paper/2307.11261/) (2023)
- [DARES: Depth Anything in Robotic Endoscopic Surgery with Self-supervised Vector-LoRA of the Foundation Model](https://mlsanity.com/paper/2408.17433/) (2024)
- [Joint one-sided synthetic unpaired image translation and segmentation for colorectal cancer prevention](https://mlsanity.com/paper/2307.11253/) (2023)

## Built on by (most cited first, 2 of 2)

- [SCARED-C: Corrected Camera Poses for Endoscopic Depth Estimation](https://mlsanity.com/paper/2605.16628/) (2026)
- [SCOPE-4D: Endoscopic 4D Geometry Foundation Models](https://mlsanity.com/paper/2610.02343/) (2026)

## Abstract (arXiv)

Deep learning has the potential to improve colonoscopy by enabling 3D reconstruction of the colon, providing a comprehensive view of mucosal surfaces and lesions, and facilitating the identification of unexplored areas. However, the development of robust methods is limited by the scarcity of large-scale ground truth data. We propose RealSynCol, a highly realistic synthetic dataset designed to replicate the endoscopic environment. Colon geometries extracted from 10 CT scans were imported into a virtual environment that closely mimics intraoperative conditions and rendered with realistic vascular textures. The resulting dataset comprises 28,130 frames, paired with ground truth depth maps, optical flow, 3D meshes, and camera trajectories. A benchmark study was conducted to evaluate the available synthetic colon datasets for the tasks of depth and pose estimation. Results demonstrate that the high realism and variability of RealSynCol significantly enhance generalization performance on clinical images, proving it to be a powerful tool for developing deep learning algorithms to support endoscopic diagnosis.