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Journal ArticleDOI

Minimal Case Relative Pose Computation Using Ray-Point-Ray Features

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TLDR
It is shown that knowing the value of the inscribed angle between the two 3D rays poses additional constraints on the relative orientation, and using the latter enables the solution of the relative pose problem with as few as 3 correspondences across the two images.
Abstract
Corners are popular features for relative pose computation with 2D-2D point correspondences. Stable corners may be formed by two 3D rays sharing a common starting point. We call such elements ray-point-ray (RPR) structures. Besides a local invariant keypoint given by the lines’ intersection, their reprojection also defines a corner orientation and an inscribed angle in the image plane. The present paper investigates such RPR features, and aims at answering the fundamental question of what additional constraints can be formed from correspondences between RPR features in two views. In particular, we show that knowing the value of the inscribed angle between the two 3D rays poses additional constraints on the relative orientation. Using the latter enables the solution of the relative pose problem with as few as 3 correspondences across the two images. We provide a detailed analysis of all minimal cases distinguishing between 90-degree RPR-structures and structures with an arbitrary, known inscribed angle. We furthermore investigate the special cases of a known directional correspondence and planar motion, the latter being solvable with only a single RPR correspondence. We complete the exposition by outlining an image processing technique for robust RPR-feature extraction. Our results suggest high practicality in man-made environments, where 90-degree RPR-structures naturally occur.

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Citations
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Revisiting the PnP Problem: A Fast, General and Optimal Solution

TL;DR: This paper revisits the classical perspective-n-point (PnP) problem, and proposes the first non-iterative O(n) solution that is fast, generally applicable and globally optimal.
Proceedings ArticleDOI

Minimal Solutions for Relative Pose With a Single Affine Correspondence

TL;DR: In this paper, the affine transformation between feature points is exploited to recover the relative camera pose under the planar motion assumption or with knowledge of a vertical direction, and a single affine correspondence is sufficient to recover camera pose.
Proceedings ArticleDOI

PLMP - Point-Line Minimal Problems in Complete Multi-View Visibility

TL;DR: In this paper, a complete classification of all minimal problems for generic arrangements of points and lines completely observed by calibrated perspective cameras is presented, and their algebraic degrees, i.e. the number of solutions, which measure their intrinsic difficulty.
Journal ArticleDOI

KM4: Visual reasoning via Knowledge Embedding Memory Model with Mutual Modulation

TL;DR: A knowledge memory embedding model with mutual modulation for visual reasoning that learns not only knowledge-based embeddings derived from key–value memory network to make the full and joint of textual and visual information, but also exploits the prior knowledge to improve the performance withknowledge-based representation learning for applying other general reasoning tasks.
Proceedings ArticleDOI

TRPLP – Trifocal Relative Pose From Lines at Points

TL;DR: This work shows in real experiment that (i) SIFT features provide good enough point-and-line correspondences for three-view reconstruction and (ii) that it can solve difficult cases with too few or too noisy tentative matches where the state of the art structure from motion initialization fails.
References
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Distinctive Image Features from Scale-Invariant Keypoints

TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Journal ArticleDOI

Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography

TL;DR: New results are derived on the minimum number of landmarks needed to obtain a solution, and algorithms are presented for computing these minimum-landmark solutions in closed form that provide the basis for an automatic system that can solve the Location Determination Problem under difficult viewing.
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Multiple view geometry in computer vision

TL;DR: In this article, the authors provide comprehensive background material and explain how to apply the methods and implement the algorithms directly in a unified framework, including geometric principles and how to represent objects algebraically so they can be computed and applied.

Multiple View Geometry in Computer Vision.

TL;DR: This book is referred to read because it is an inspiring book to give you more chance to get experiences and also thoughts and it will show the best book collections and completed collections.
Proceedings ArticleDOI

A Combined Corner and Edge Detector

TL;DR: The problem the authors are addressing in Alvey Project MMI149 is that of using computer vision to understand the unconstrained 3D world, in which the viewed scenes will in general contain too wide a diversity of objects for topdown recognition techniques to work.