Avaliação de Métodos de Correspondência de Pontos de Interesse para Reconstrução Projetiva de Cenas 3D
(Evaluation of Interest Point Matching Methods for Projective Reconstruction of 3D Scenes)
Darlan N Brito (firstname.lastname@example.org)1, Cristiano F G Nunes (email@example.com)1, Flávio L C Pádua (firstname.lastname@example.org)1, Anisio Lacerda (email@example.com)1
1Centro Federal de Educação Tecnológica de Minas Gerais
This paper appears in: Revista IEEE América Latina
Publication Date: March 2016
Volume: 14, Issue: 3
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.
Point Matching Methods, Camera Self-Calibration, Projective Reconstruction
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