Authors:
Silvio Filipe
and
Luís A. Alexandre
Affiliation:
IT - Instituto de Telecomunicações and University of Beira Interior, Portugal
Keyword(s):
3D Keypoints, 3D Interest Points, 3D Object Recognition, Performance Evaluation.
Related
Ontology
Subjects/Areas/Topics:
Active and Robot Vision
;
Applications
;
Color and Texture Analyses
;
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Geometry and Modeling
;
Image and Video Analysis
;
Image Registration
;
Image-Based Modeling
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Robotics
;
Segmentation and Grouping
;
Shape Representation and Matching
;
Software Engineering
;
Stereo Vision and Structure from Motion
Abstract:
When processing 3D point cloud data, features must be extracted from a small set of points, usually called keypoints. This is done to avoid the computational complexity required to extract features from all points in a point cloud. There are many keypoint detectors and this suggests the need of a comparative evaluation. When the keypoint detectors are applied to 3D objects, the aim is to detect a few salient structures which can be used, instead of the whole object, for applications like object registration, retrieval and data simplification. In this paper, we propose to do a description and evaluation of existing keypoint detectors in a public available point cloud library with real objects and perform a comparative evaluation on 3D point clouds. We evaluate the invariance of the 3D keypoint detectors according to rotations, scale changes and translations. The evaluation criteria used are the absolute and the relative repeatability rate. Using these criteria, we evaluate the robustn
ess of the detectors with respect to changes of point-of-view. In our experiments, the method that achieved better repeatability rate was the ISS3D method.
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