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picture1_Geometry Pdf 166908 | Notes Item Download 2023-01-25 00-05-10


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File: Geometry Pdf 166908 | Notes Item Download 2023-01-25 00-05-10
notes on multi view geometry in computer vision yufei ye march 10 2020 2 intro uncalibrate problems has achieved great progress in recent decade given2iamges computematchesbetweentheimages andthe3dposition of the points ...

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           Notes on Multi-view Geometry in Computer
                      Vision
                      Yufei Ye
                     March 10, 2020
                 2
           intro
           uncalibrate problems has achieved great progress in recent decade:
             • Given2iamges,computematchesbetweentheimages,andthe3Dposition
              of the points that generate these matches and the cameras that generate
              the images.
             • Given three images, and no other information, similarly compute the
              matches between images of points and lines, and the position in 3D of
              these points and lines and the cameras.
             • Compute the epipolar geometry of a stereo rig, and trifocal geometry of a
              trinocularrig, without requiring a calibration object.
             • Compute the internal calibration of a camera from a sequence of images
              of natural scenes (i.e. calibration “on the fly”.
           Whythese achievement?
             • the error that hsould be minimized in over-determined system
             • robust estimation
           Solved problem:
             • Estimation of the multifocal tensors from image point correspondences,
              particularly the fundamental matrix and trifocal tensors (the quadrifocal
              tensor having not received so much attention).
             • Extraction of the camera matrices from these tensors, and subsequent
              projective reconstruction from two, three and four views.
           More to learn:
             • bundle adjustment to solve more general reconstruction problems.
             • Metric (Euclidean) reconstruction given minimal assumptions on the cam-
              era matrices.
             • Automatic detection of correspondences in image sequences, and elimina-
              tion of outliers and false matches using the multifocal tensor relationships.
                            3
                 4
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...Notes on multi view geometry in computer vision yufei ye march intro uncalibrate problems has achieved great progress recent decade giveniamges computematchesbetweentheimages andthedposition of the points that generate these matches and cameras images given three no other information similarly compute between lines position d epipolar a stereo rig trifocal trinocularrig without requiring calibration object internal camera from sequence natural scenes i e y whythese achievement error hsould be minimized over determined system robust estimation solved problem multifocal tensors image point correspondences particularly fundamental matrix quadrifocal tensor having not received so much attention extraction matrices subsequent projective reconstruction two four views more to learn bundle adjustment solve general metric euclidean minimal assumptions cam era automatic detection sequences elimina tion outliers false using relationships...

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