mlsanityData to 7 Oct 2026

SuperPoint features in endoscopy

arXiv 2203.04302 ↗First posted 8 Mar 2022eess.IV cs.CVcited by 5

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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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 19 of its 35 references that are in the map. First posted on arXiv 8 Mar 2022.

Builds on

19 of 35 references.

References · 19

  1. 2019SuperGlue: Learning Feature Matching with Graph Neural Networkscited by 1,437
  2. 2017SuperPoint: Self-Supervised Interest Point Detection and Descriptioncited by 1,313
  3. 2021LoFTR: Detector-Free Local Feature Matching with Transformerscited by 974
  4. 2016LIFT: Learned Invariant Feature Transformcited by 305
  5. 2020Image Matching across Wide Baselines: From Paper to Practicecited by 224
  6. 2017Working hard to know your neighbor's margins: Local descriptor learning losscited by 212
  7. 2019R2D2: Repeatable and Reliable Detector and Descriptorcited by 206
  8. 2021COTR: Correspondence Transformer for Matching Across Imagescited by 182
  9. 2018LF-Net: Learning Local Features from Imagescited by 151
  10. 2020Patch2Pix: Epipolar-Guided Pixel-Level Correspondencescited by 128
  11. 2019Key.Net: Keypoint Detection by Handcrafted and Learned CNN Filterscited by 117
  12. 2017Repeatability Is Not Enough: Learning Affine Regions via Discriminabilitycited by 57
7 more
  1. 2017MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Networkcited by 56
  2. 2016Quad-networks: unsupervised learning to rank for interest point detectioncited by 36
  3. 2020D2D: Keypoint Extraction with Describe to Detect Approachcited by 34
  4. 2020Deep Keypoint-Based Camera Pose Estimation with Geometric Constraintscited by 29
  5. 2020Reconstructing Sinus Anatomy from Endoscopic Video -- Towards a Radiation-free Approach for Quantitative Longitudinal Assessmentcited by 23
  6. 2020SD-DefSLAM: Semi-Direct Monocular SLAM for Deformable and Intracorporeal Scenescited by 21
  7. 2020Extremely Dense Point Correspondences using a Learned Feature Descriptorcited by 20

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 3; 2024: 0; 2025: 0; 2026: 1.

5 papers.

  1. 2022Bimodal Camera Pose Prediction for Endoscopycited by 20
  2. 2023SENDD: Sparse Efficient Neural Depth and Deformation for Tissue Trackingcited by 14
  3. 2023RIDE: Self-Supervised Learning of Rotation-Equivariant Keypoint Detection and Invariant Description for Endoscopycited by 5
  4. 2023DAC: Detector-Agnostic Spatial Covariances for Deep Local Featurescited by 3
  5. 2026SuperPoint-E: local features for 3D reconstruction via tracking adaptation in endoscopycited by 0