Browse > Computer Vision > Interest Point Detection

Interest Point Detection

6 papers with code · Computer Vision

Leaderboards

You can find evaluation results in the subtasks. You can also submitting evaluation metrics for this task.

Greatest papers with code

SuperPoint: Self-Supervised Interest Point Detection and Description

20 Dec 2017MagicLeapResearch/SuperPointPretrainedNetwork

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.

DOMAIN ADAPTATION HOMOGRAPHY ESTIMATION

R2D2: Reliable and Repeatable Detector and Descriptor

NeurIPS 2019 naver/r2d2

We thus propose to jointly learn keypoint detection and description together with a predictor of the local descriptor discriminativeness.

ATARI GAMES INTEREST POINT DETECTION KEYPOINT DETECTION METRIC LEARNING

USIP: Unsupervised Stable Interest Point Detection from 3D Point Clouds

ICCV 2019 lijx10/USIP

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 without the need for any ground truth training data.

INTEREST POINT DETECTION

SIPs: Succinct Interest Points from Unsupervised Inlierness Probability Learning

3 May 2018uzh-rpg/sips2_open

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.

INTEREST POINT DETECTION POSE ESTIMATION VISUAL ODOMETRY

CAE-LO: LiDAR Odometry Leveraging Fully Unsupervised Convolutional Auto-Encoder for Interest Point Detection and Feature Description

6 Jan 2020SRainGit/CAE-LO

As an important technology in 3D mapping, autonomous driving, and robot navigation, LiDAR odometry is still a challenging task.

AUTONOMOUS DRIVING INTEREST POINT DETECTION ROBOT NAVIGATION

Neural Outlier Rejection for Self-Supervised Keypoint Learning

23 Dec 2019TRI-ML/KP2D

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 improve keypoint matching.

HOMOGRAPHY ESTIMATION KEYPOINT DETECTION VISUAL ODOMETRY