https://github.com/mguti97/no-bells-just-whistles
공식 코드PnLCalib: Sports Field Registration via Points and Lines Optimization
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Homography estimation is a technique used in computer vision and image processing to find the relationship between two images of the same scene, but captured from different viewpoints. It is used to align images, correct for perspective distortions, or perform image stitching. In order to estimate the homography, a set of corresponding points between the two images must be found, and a mathematical model must be fit to these points. There are various algorithms and techniques that can be used to perform homography estimation, including direct methods, RANSAC, and machine learning-based approaches.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
PnLCalib: Sports Field Registration via Points and Lines Optimization
PnLCalib: Sports Field Registration via Points and Lines Optimization
Depth-Aware Multi-Grid Deep Homography Estimation with Contextual Correlation
A Gis Aided Approach for Geolocalizing an Unmanned Aerial System Using Deep Learning
Learning Knowledge-Rich Sequential Model for Planar Homography Estimation in Aerial Video
Video-based Sequential Bayesian Homography Estimation for Soccer Field Registration
SCPNet: Unsupervised Cross-modal Homography Estimation via Intra-modal Self-supervised Learning
Deep Homography Estimation in Dynamic Surgical Scenes for Laparoscopic Camera Motion Extraction
3DG-STFM: 3D Geometric Guided Student-Teacher Feature Matching
Unsupervised Homography Estimation on Multimodal Image Pair via Alternating Optimization
Real-Time Facial Expression Emoji Masking with Convolutional Neural Networks and Homography
Unsupervised Deep Homography: A Fast and Robust Homography Estimation Model
Towards a Unified Approach to Homography Estimation Using Image Features and Pixel Intensities
Fine-Grained Cross-View Geo-Localization Using a Correlation-Aware Homography Estimator
Line as a Visual Sentence: Context-aware Line Descriptor for Visual Localization
UnsuperPoint: End-to-end Unsupervised Interest Point Detector and Descriptor
SuperPoint: Self-Supervised Interest Point Detection and Description
SuperPoint: Self-Supervised Interest Point Detection and Description
SuperPoint: Self-Supervised Interest Point Detection and Description
SuperPoint: Self-Supervised Interest Point Detection and Description
SuperPoint: Self-Supervised Interest Point Detection and Description
SuperPoint: Self-Supervised Interest Point Detection and Description
SuperPoint: Self-Supervised Interest Point Detection and Description
SuperPoint: Self-Supervised Interest Point Detection and Description
SuperPoint: Self-Supervised Interest Point Detection and Description
SuperPoint: Self-Supervised Interest Point Detection and Description
Depth-Aware Multi-Grid Deep Homography Estimation with Contextual Correlation
Depth-Aware Multi-Grid Deep Homography Estimation with Contextual Correlation
Depth-Aware Multi-Grid Deep Homography Estimation with Contextual Correlation
SuperPoint: Self-Supervised Interest Point Detection and Description
SuperPoint: Self-Supervised Interest Point Detection and Description
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.