Interest Point Detection

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

1 dataset whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

1 subtask in the archive's task tree.

Most implemented papers archive 2025-07-28

11 shown of 11 papers with code (26 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• SuperPoint: Self-Supervised Interest Point Detection and Description
  20 Dec 2017 36 repositories listed Syntology ran 11 of 11 samples · 0 unverified · 6 pointer-only (licence)
  This paper presents a self-supervised framework for training interest point detectors and descriptors suitable for a large number of multiple-view geometry problems in computer vision.
• Neural Outlier Rejection for Self-Supervised Keypoint Learning
  23 Dec 2019 2 repositories listed
  By making the sampling of inlier-outlier sets from point-pair correspondences fully differentiable within the keypoint learning framework, we show that are able to simultaneously self-supervise keypoint description and…
• R2D2: Reliable and Repeatable Detector and Descriptor
  1 Dec 2019 2 repositories listed
  We thus propose to jointly learn keypoint detection and description together with a predictor of the local descriptor discriminativeness.
• Rethinking Low-level Features for Interest Point Detection and Description
  1 Nov 2022 1 repository listed
  Although great efforts have been made for interest point detection and description, the current learning-based methods that use high-level features from the higher layers of Convolutional Neural Networks (CNN) do not…
• ZippyPoint: Fast Interest Point Detection, Description, and Matching through Mixed Precision Discretization
  7 Mar 2022 1 repository listed
  Efficient detection and description of geometric regions in images is a prerequisite in visual systems for localization and mapping.
• MultiPoint: Cross-spectral registration of thermal and optical aerial imagery
  18 Nov 2020 1 repository listed
  This model is then deployed for fast and accurate online interest point detection.
• DELTAS: Depth Estimation by Learning Triangulation And densification of Sparse points
  19 Mar 2020 1 repository listed
  Cost volume based approaches employing 3D convolutional neural networks (CNNs) have considerably improved the accuracy of MVS systems.
• CAE-LO: LiDAR Odometry Leveraging Fully Unsupervised Convolutional Auto-Encoder for Interest Point Detection and Feature Description
  6 Jan 2020 1 repository listed
  As an important technology in 3D mapping, autonomous driving, and robot navigation, LiDAR odometry is still a challenging task.
• R2D2: Repeatable and Reliable Detector and Descriptor
  14 Jun 2019 1 repository listed
  In this work, we argue that salient regions are not necessarily discriminative, and therefore can harm the performance of the description.
• USIP: Unsupervised Stable Interest Point Detection from 3D Point Clouds
  30 Mar 2019 1 repository listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  In this paper, we propose the USIP detector: an Unsupervised Stable Interest Point detector that can detect highly repeatable and accurately localized keypoints from 3D point clouds under arbitrary transformations…
• SIPs: Succinct Interest Points from Unsupervised Inlierness Probability Learning
  3 May 2018 1 repository listed
  In certain cases, our detector is able to obtain an equivalent amount of inliers with as little as 60% of the amount of points of other detectors.

Syntology lines on 2 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.