Evaluation of interest point matching methods for projective reconstruction of 3d scenes.
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Date
2016
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Abstract
This work evaluates the application of different
state-of-the-art methods for interest point matching, aiming the
robust and efficient projective reconstruction of three-dimensional
scenes. Projective reconstruction refers to the computation of the
structure of a scene from images taken with uncalibrated cameras.
To achieve this goal, it is essential the usage of an effective point
matching algorithm. Even though several point matching methods
have been proposed in the literature, their impacts in the
projective reconstruction task have not yet been carefully studied.
Our evaluation uses as criterion the estimated epipolar,
reprojection and reconstruction errors, as well as the running
times of the algorithms. Specifically, we compare five different
techniques: SIFT, SURF, ORB, BRISK and FREAK. Our
experiments show that binary algorithms such as, ORB and
BRISK, are so accurate as float point algorithms like SIFT and
SURF, nevertheless, with smaller computational cost.
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Keywords
Point matching methods, Camera self-calibration, Projective reconstruction
Citation
BRITO, D. N. de et al. Evaluation of interest point matching methods for projective reconstruction of 3d scenes. IEEE Latin America Transactions, v. 14, p. 1393-1400, 2016. Disponível em: <https://ieeexplore.ieee.org/document/7459626>. Acesso em: 02 out. 2017.