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[IEEE 2018 International Conference on 3D Vision (3DV) - Verona (2018.9.5-2018.9.8)] 2018 International Conference on 3D Vision (3DV) - Four- and Seven-Point Relative Camera Pose from Oriented Features

DOI:10.1109/3DV.2018.00034 出版年份:2018 更新时间:2025-09-10 09:29:36
摘要: Determining relative camera pose is a fundamental problem in computer vision, and pose is often computed from feature correspondences. For point features, a minimum of five correspondences are required to determine the pose between two calibrated cameras, and eight corresponding points can be used to form a linear solution. However, most feature detectors used in practice produce points with an associated orientation. This work demonstrates that with oriented features the relative pose of two cameras can be computed from just four point correspondences, or seven with a linear solution. These new four- and seven-point algorithms do not require any additional sensors or parameters, but exploit information (feature orientation) that is already computed by most existing structure-from-motion systems. On the DTU multi-view stereo data set the four-point algorithm is shown to be 55% faster than the five-point algorithm, and the seven-point linear algorithm gives a 43% speed improvement over the eight-point algorithm.
作者: Steven Mills
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Investigating the use of oriented features to reduce the number of point correspondences required for relative camera pose estimation.

The four- and seven-point algorithms for relative camera pose estimation using oriented features significantly reduce computation time compared to traditional methods, with similar or improved accuracy in most cases. The four-point algorithm is 55% faster than the five-point algorithm, and the seven-point algorithm provides a 43% speed improvement over the eight-point algorithm on the DTU data set. These methods exploit feature orientation information already available in most structure-from-motion systems, without requiring additional sensors or parameters.

The proposed methods are most sensitive to errors in cases of small rotation combined with translation along the camera axis, which is common in vehicle navigation tasks. The accuracy and robustness of the methods depend on the reliability of feature orientation estimates.

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