Relative Pose from SIFT Features

• URL: http://arxiv.org/abs/2203.07930v1
• Date: Tue, 15 Mar 2022 14:16:39 GMT
• Title: Relative Pose from SIFT Features
• Authors: Daniel Barath, Zuzana Kukelova
• Abstract summary: We derive a new linear constraint relating the unknown elements of the fundamental matrix and the orientation and scale. The proposed constraint is tested on a number of problems in a synthetic environment and on publicly available real-world datasets on more than 80000 image pairs.
• Score: 50.81749304115036
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper proposes the geometric relationship of epipolar geometry and orientation- and scale-covariant, e.g., SIFT, features. We derive a new linear constraint relating the unknown elements of the fundamental matrix and the orientation and scale. This equation can be used together with the well-known epipolar constraint to, e.g., estimate the fundamental matrix from four SIFT correspondences, essential matrix from three, and to solve the semi-calibrated case from three correspondences. Requiring fewer correspondences than the well-known point-based approaches (e.g., 5PT, 6PT and 7PT solvers) for epipolar geometry estimation makes RANSAC-like randomized robust estimation significantly faster. The proposed constraint is tested on a number of problems in a synthetic environment and on publicly available real-world datasets on more than 80000 image pairs. It is superior to the state-of-the-art in terms of processing time while often leading to more accurate results.

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