https://github.com/teddykoker/unsupervised-deep-homography
Unsupervised Deep Homography: A Fast and Robust Homography Estimation Model
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.
Unsupervised Deep Homography: A Fast and Robust Homography Estimation Model
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
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.