Open AccessDETECTION OF GEOMETRIC KEYPOINTS AND ITS APPLICATION TO POINT CLOUD COARSE REGISTRATION
Author(s) -
M. Bueno,
J. Martínez-Sánchez,
H. González-Jorge,
Henrique Lorenzo
Publication year - 2016
Publication title -
the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.264
H-Index - 71
eISSN - 1682-1777
pISSN - 1682-1750
DOI - 10.5194/isprsarchives-xli-b3-187-2016
Subject(s) - point cloud , computer science , artificial intelligence , computer vision , preprocessor , frame (networking) , point (geometry) , position (finance) , object (grammar) , pattern recognition (psychology) , mathematics , telecommunications , geometry , finance , economics
Acquisition of large scale scenes, frequently, involves the storage of large amount of data, and also, the placement of several scan positions to obtain a complete object. This leads to a situation with a different coordinate system in each scan position. Thus, a preprocessing of it to obtain a common reference frame is usually needed before analysing it. Automatic point cloud registration without locating artificial markers is a challenging field of study. The registration of millions or billions of points is a demanding task. Subsampling the original data usually solves the situation, at the cost of reducing the precision of the final registration. In this work, a study of the subsampling via the detection of keypoints and its capability to apply in coarse alignment is performed. The keypoints obtained are based on geometric features of each individual point, and are extracted using the Difference of Gaussians approach over 3D data. The descriptors include features as eigenentropy, change of curvature and planarity. Experiments demonstrate that the coarse alignment, obtained through these keypoints outperforms the coarse registration root mean squared error of an operator by 3 - 5 cm. The applicability of these keypoints is tested and verified in five different case studies.