Сравнение методов

	Стереосопоставление (стерео-матчинг)	Метод Лукаса-Канаде
Область	Компьютерное зрение	Компьютерное зрение
Семейство	Machine learning	Machine learning
Год появления	1990s	1981
Автор метода	David Scharstein and Richard Szeliski	Bruce Lucas and Takeo Kanade
Тип	Depth estimation and 3D vision	Optical flow and tracking
Основополагающий источник	Scharstein, D., & Szeliski, R. (2002). A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision, 47(1), 7–42.	Lucas, B. D., & Kanade, T. (1981). An iterative image registration technique with an application to stereo vision. Proceedings of the Seventh International Joint Conference on Artificial Intelligence (IJCAI), 674–679.
Другие названия	Stereo correspondence, Disparity estimation	Lucas-Kanade method, Sparse optical flow
Связанные	5	5
Сводка	Stereo matching is a computer vision technique for recovering depth information by finding corresponding points between a pair of stereo images (taken from slightly different viewpoints). By locating the same scene feature in both images and measuring the disparity (horizontal shift), stereo matching reconstructs 3D structure using the principles of triangulation.	The Lucas-Kanade method, introduced by Bruce Lucas and Takeo Kanade in 1981, is a foundational technique for estimating optical flow—the apparent motion of objects in image sequences. By computing pixel-level motion vectors, the Lucas-Kanade algorithm tracks feature displacements between consecutive frames, enabling object tracking, motion estimation, and video analysis.