# SuperPoint features in endoscopy

arXiv: https://arxiv.org/abs/2203.04302 · HTML: https://mlsanity.com/paper/2203.04302/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 35 references that are in the map.
- First posted on arXiv 8 Mar 2022.

## Builds on (most cited first, 19 of 19)

- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [LoFTR: Detector-Free Local Feature Matching with Transformers](https://mlsanity.com/paper/2104.00680/) (2021)
- [LIFT: Learned Invariant Feature Transform](https://mlsanity.com/paper/1603.09114/) (2016)
- [Image Matching across Wide Baselines: From Paper to Practice](https://mlsanity.com/paper/2003.01587/) (2020)
- [Working hard to know your neighbor's margins: Local descriptor learning loss](https://mlsanity.com/paper/1705.10872/) (2017)
- [R2D2: Repeatable and Reliable Detector and Descriptor](https://mlsanity.com/paper/1906.06195/) (2019)
- [COTR: Correspondence Transformer for Matching Across Images](https://mlsanity.com/paper/2103.14167/) (2021)
- [LF-Net: Learning Local Features from Images](https://mlsanity.com/paper/1805.09662/) (2018)
- [Patch2Pix: Epipolar-Guided Pixel-Level Correspondences](https://mlsanity.com/paper/2012.01909/) (2020)
- [Key.Net: Keypoint Detection by Handcrafted and Learned CNN Filters](https://mlsanity.com/paper/1904.00889/) (2019)
- [Repeatability Is Not Enough: Learning Affine Regions via Discriminability](https://mlsanity.com/paper/1711.06704/) (2017)
- [MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network](https://mlsanity.com/paper/1707.02485/) (2017)
- [Quad-networks: unsupervised learning to rank for interest point detection](https://mlsanity.com/paper/1611.07571/) (2016)
- [D2D: Keypoint Extraction with Describe to Detect Approach](https://mlsanity.com/paper/2005.13605/) (2020)
- [Deep Keypoint-Based Camera Pose Estimation with Geometric Constraints](https://mlsanity.com/paper/2007.15122/) (2020)
- [Reconstructing Sinus Anatomy from Endoscopic Video -- Towards a Radiation-free Approach for Quantitative Longitudinal Assessment](https://mlsanity.com/paper/2003.08502/) (2020)
- [SD-DefSLAM: Semi-Direct Monocular SLAM for Deformable and Intracorporeal Scenes](https://mlsanity.com/paper/2010.09409/) (2020)
- [Extremely Dense Point Correspondences using a Learned Feature Descriptor](https://mlsanity.com/paper/2003.00619/) (2020)

## Built on by (most cited first, 6 of 6)

- [Bimodal Camera Pose Prediction for Endoscopy](https://mlsanity.com/paper/2204.04968/) (2022)
- [SENDD: Sparse Efficient Neural Depth and Deformation for Tissue Tracking](https://mlsanity.com/paper/2305.06477/) (2023)
- [RIDE: Self-Supervised Learning of Rotation-Equivariant Keypoint Detection and Invariant Description for Endoscopy](https://mlsanity.com/paper/2309.09563/) (2023)
- [DAC: Detector-Agnostic Spatial Covariances for Deep Local Features](https://mlsanity.com/paper/2305.12250/) (2023)
- [SuperPoint-E: local features for 3D reconstruction via tracking adaptation in endoscopy](https://mlsanity.com/paper/2602.04108/) (2026)
- [SCOPE-4D: Endoscopic 4D Geometry Foundation Models](https://mlsanity.com/paper/2610.02343/) (2026)

## Abstract (arXiv)

There is often a significant gap between research results and applicability in routine medical practice. This work studies the performance of well-known local features on a medical dataset captured during routine colonoscopy procedures. Local feature extraction and matching is a key step for many computer vision applications, specially regarding 3D modelling. In the medical domain, handcrafted local features such as SIFT, with public pipelines such as COLMAP, are still a predominant tool for this kind of tasks. We explore the potential of the well known self-supervised approach SuperPoint, present an adapted variation for the endoscopic domain and propose a challenging evaluation framework. SuperPoint based models achieve significantly higher matching quality than commonly used local features in this domain. Our adapted model avoids features within specularity regions, a frequent and problematic artifact in endoscopic images, with consequent benefits for matching and reconstruction results.