Abstract: The invention relates to a method (100) for recognizing patterns (210) in image data (310), comprising the following steps: providing (101) the image data (310), determining (102) gradient information (410) on the basis of the image data (310) provided, wherein the gradient information (410) correlates with at least one image gradient in the image data (310) provided, determining (103) edge information (420) on the basis of the image data (310) provided, wherein the edge information (420) represents at least one or multiple edge image points (430) in the image data (310) provided, evaluating (104) the image data (310) provided on the basis of the gradient information (410) and the edge information (420), providing (105) at least one or multiple pattern representations (450) for the recognition of the pattern (210) on the basis of the evaluation (104).

Abstract: Scenes reconstruction may be performed using videos that capture the scenes at high resolution and frame rate. Scene reconstruction may be associated with determining camera orientation and/or location (“camera pose”) throughout the video, three-dimensional coordinates of feature points detected in frames of the video, and/or other information. Individual videos may have multiple frames. Feature points may be detected in, and tracked over, the frames. Estimations of camera pose may be made for individual subsets of frames. One or more estimations of camera pose may be determined as fixed estimations. The estimated camera poses for the frames included in the subsets of frames may be updated based on the fixed estimations. Camera pose for frames not included in the subsets of frames may be determined to provide globally consistent camera poses and three-dimensional coordinates for feature points of the video.

Abstract: Scenes reconstruction may be performed using videos that capture the scenes at high resolution and frame rate. Scene reconstruction may be associated with determining camera orientation and/or location (“camera pose”) throughout the video, three-dimensional coordinates of feature points detected in frames of the video, and/or other information. Individual videos may have multiple frames. Feature points may be detected in, and tracked over, the frames. Estimations of camera pose may be made for individual subsets of frames. One or more estimations of camera pose may be determined as fixed estimations. The estimated camera poses for the frames included in the subsets of frames may be updated based on the fixed estimations. Camera pose for frames not included in the subsets of frames may be determined to provide globally consistent camera poses and three-dimensional coordinates for feature points of the video.

Abstract: Scenes reconstruction may be performed using videos that capture the scenes at high resolution and frame rate. Scene reconstruction may be associated with determining camera orientation and/or location (“camera pose”) throughout the video, three-dimensional coordinates of feature points detected in frames of the video, and/or other information. Individual videos may have multiple frames. Feature points may be detected in, and tracked over, the frames. Estimations of camera pose may be made for individual subsets of frames. One or more estimations of camera pose may be determined as fixed estimations. The estimated camera poses for the frames included in the subsets of frames may be updated based on the fixed estimations. Camera pose for frames not included in the subsets of frames may be determined to provide globally consistent camera poses and three-dimensional coordinates for feature points of the video.