# SERV-CT: A disparity dataset from CT for validation of endoscopic 3D reconstruction

arXiv: https://arxiv.org/abs/2012.11779 · HTML: https://mlsanity.com/paper/2012.11779/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2020.

## Built on by (most cited first, 23 of 23)

- [Colonoscopy 3D Video Dataset with Paired Depth from 2D-3D Registration](https://mlsanity.com/paper/2206.08903/) (2022)
- [Tracking and Mapping in Medical Computer Vision: A Review](https://mlsanity.com/paper/2310.11475/) (2023)
- [Surgical Tattoos in Infrared: A Dataset for Quantifying Tissue Tracking and Mapping](https://mlsanity.com/paper/2309.16782/) (2023)
- [Transferring Relative Monocular Depth to Surgical Vision with Temporal Consistency](https://mlsanity.com/paper/2403.06683/) (2024)
- [Learning Feature Descriptors for Pre- and Intra-operative Point Cloud Matching for Laparoscopic Liver Registration](https://mlsanity.com/paper/2211.03688/) (2022)
- [EndoOmni: Zero-Shot Cross-Dataset Depth Estimation in Endoscopy by Robust Self-Learning from Noisy Labels](https://mlsanity.com/paper/2409.05442/) (2024)
- [MonoPCC: Photometric-invariant Cycle Constraint for Monocular Depth Estimation of Endoscopic Images](https://mlsanity.com/paper/2404.16571/) (2024)
- [On the Sins of Image Synthesis Loss for Self-supervised Depth Estimation](https://mlsanity.com/paper/2109.06163/) (2021)
- [Bidirectional Semi-supervised Dual-branch CNN for Robust 3D Reconstruction of Stereo Endoscopic Images via Adaptive Cross and Parallel Supervisions](https://mlsanity.com/paper/2210.08291/) (2022)
- [BDIS: Bayesian Dense Inverse Searching Method for Real-Time Stereo Surgical Image Matching](https://mlsanity.com/paper/2205.03133/) (2022)
- [Leveraging a realistic synthetic database to learn Shape-from-Shading for estimating the colon depth in colonoscopy images](https://mlsanity.com/paper/2311.05021/) (2023)
- [Depth-Aware Endoscopic Video Inpainting](https://mlsanity.com/paper/2407.02675/) (2024)
- [NeRFscopy: Neural Radiance Fields for in-vivo Time-Varying Tissues from Endoscopy](https://mlsanity.com/paper/2602.15775/) (2026)
- [Robotic Arm Platform for Multi-View Image Acquisition and 3D Reconstruction in Minimally Invasive Surgery](https://mlsanity.com/paper/2410.11703/) (2024)
- [Endoscopic Depth Estimation Based on Deep Learning: A Survey](https://mlsanity.com/paper/2507.20881/) (2025)
- [EndoUFM: Utilizing Foundation Models for Monocular depth estimation of endoscopic images](https://mlsanity.com/paper/2508.17916/) (2025)
- [A Disparity Refinement Framework for Learning-based Stereo Matching Methods in Cross-domain Setting for Laparoscopic Images](https://mlsanity.com/paper/2302.02294/) (2023)
- [EndoGMDE: Generalizable Monocular Depth Estimation with Mixture of Low-Rank Experts for Diverse Endoscopic Scenes](https://mlsanity.com/paper/2509.01206/) (2025)
- [The Dresden Dataset for 4D Reconstruction of Non-Rigid Abdominal Surgical Scenes](https://mlsanity.com/paper/2603.02985/) (2026)
- [Spectral Rectification for Parameter-Efficient Adaptation of Foundation Models in Colonoscopy Depth Estimation](https://mlsanity.com/paper/2603.15374/) (2026)
- [Boosting Generalizable Depth Estimation in Endoscopy by Mixture of Lightweight Experts and Intrinsic Image Alignment](https://mlsanity.com/paper/2608.00415/) (2026)
- [MV-dVRK: A Multi-Viewpoint Benchmark for Spatial Surgical Perception](https://mlsanity.com/paper/2609.02717/) (2026)
- [EndoPrior-GS: Dynamic Endoscopic Reconstruction with a Joint Texture Prior](https://mlsanity.com/paper/2609.37874/) (2026)

## Abstract (arXiv)

In computer vision, reference datasets have been highly successful in promoting algorithmic development in stereo reconstruction. Surgical scenes gives rise to specific problems, including the lack of clear corner features, highly specular surfaces and the presence of blood and smoke. Publicly available datasets have been produced using CT and either phantom images or biological tissue samples covering a relatively small region of the endoscope field-of-view. We present a stereo-endoscopic reconstruction validation dataset based on CT (SERV-CT). Two it ex vivo small porcine full torso cadavers were placed within the view of the endoscope with both the endoscope and target anatomy visible in the CT scan. Orientation of the endoscope was manually aligned to the stereoscopic view. Reference disparities and occlusions were calculated for 8 stereo pairs from each sample. For the second sample an RGB surface was acquired to aid alignment of smooth, featureless surfaces. Repeated manual alignments showed an RMS disparity accuracy of ~2 pixels and a depth accuracy of ~2mm. The reference dataset includes endoscope image pairs with corresponding calibration, disparities, depths and occlusions covering the majority of the endoscopic image and a range of tissue types. Smooth specular surfaces and images with significant variation of depth are included. We assessed the performance of various stereo algorithms from online available repositories. There is a significant variation between algorithms, highlighting some of the challenges of surgical endoscopic images. The SERV-CT dataset provides an easy to use stereoscopic validation for surgical applications with smooth reference disparities and depths with coverage over the majority of the endoscopic images. This complements existing resources well and we hope will aid the development of surgical endoscopic anatomical reconstruction algorithms.