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Reconstruction from Multiple Views Chapter 6 Prof. Daniel Cremers Reconstruction from Multiple Views FromTwoViewsto Multiple View Geometry Multiple Views Preimage & Coimage Summer2022 from Multiple Views FromPreimagesto RankConstraints Geometric Interpretation TheMultiple-view Matrix Relation to Epipolar Constraints Multiple-View Reconstruction Algorithms Multiple-View Reconstruction of Prof. Daniel Cremers Lines Chair of Computer Vision and Artificial Intelligence Departments of Informatics & Mathematics Technical University of Munich updatedApril23,2022 1/43 Overview Reconstruction from Multiple Views Prof. Daniel Cremers 1 FromTwoViewstoMultipleViews 2 Preimage&CoimagefromMultipleViews FromTwoViewsto Multiple Views Preimage & Coimage 3 FromPreimagestoRankConstraints from Multiple Views FromPreimagesto RankConstraints 4 GeometricInterpretation Geometric Interpretation TheMultiple-view 5 TheMultiple-view Matrix Matrix Relation to Epipolar Constraints 6 Relation to Epipolar Constraints Multiple-View Reconstruction Algorithms 7 Multiple-View Reconstruction Algorithms Multiple-View Reconstruction of Lines 8 Multiple-View Reconstruction of Lines updatedApril23,2022 2/43 Multiple-View Geometry Reconstruction from Multiple Views In this section, we deal with the problem of 3D reconstruction Prof. Daniel Cremers given multiple views of a static scene, either obtained simultaneously, or sequentially from a moving camera. Thekeyideaisthat the three-view scenario allows to obtain FromTwoViewsto moremeasurementstoinfer the same number of 3D Multiple Views coordinates. For example, given two views of a single 3D point, Preimage & Coimage from Multiple Views wehavefourmeasurements(x-andy-coordinate in each FromPreimagesto view), while the three-view case provides 6 measurements per RankConstraints Geometric point correspondence. As a consequence, the estimation of Interpretation motion and structure will generally be more constrained when TheMultiple-view reverting to additional views. Matrix Relation to Epipolar Thethree-view case has traditionally been addressed by the Constraints Multiple-View so-called trifocal tensor [Hartley ’95, Vieville ’93] which Reconstruction Algorithms generalizes the fundamental matrix. This tensor – as the Multiple-View fundamental matrix – does not depend on the scene structure Reconstruction of Lines but rather on the inter-frame camera motion. It captures a trilinear relationship between three views of the same 3D point or line [Liu, Huang ’86, Spetsakis, Aloimonos ’87]. updatedApril23,2022 3/43 Trifocal Tensor versus Multiview Matrices Reconstruction from Multiple Views Prof. Daniel Cremers Traditionally the trilinear relations were captured by generalizing the concept of the Fundamental Matrix to that of a Trifocal Tensor. It was developed among others by [Liu and Huang’86], [Spetsakis, Aloimonos ’87]. The use of tensors waspromotedby[Vieville ’93] and [Hartley ’95]. Bilinear, FromTwoViewsto Multiple Views trilinear and quadrilinear constraints were formulated in [Triggs Preimage & Coimage ’95]. This line of work is summarized in the books: from Multiple Views FromPreimagesto Faugeras and Luong, “The Geometry of Multiple Views”, 2001 RankConstraints Geometric and Interpretation TheMultiple-view Hartley and Zisserman, “Multiple View Geometry”, 2001, 2003. Matrix Relation to Epipolar In the following, however, we stick with a matrix notation for the Constraints Multiple-View multiview scenario. This approach makes use of matrices and Reconstruction rank constraints on these matrices to impose the constraints Algorithms Multiple-View from multiple views. Such rank constraints were used by many Reconstruction of authors, among others in [Triggs ’95] and in [Heyden, Åström Lines ’97]. This line of work is summarized in the book Ma, Soatto, Kosecka, Sastry, “An Invitation to 3D Vision”, 2004. updatedApril23,2022 4/43
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