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Embrace descriptors that use point pairs feature
Visual ComputerDongjie Li, Xu Li, Changfeng Li
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3D Feature Detector-Descriptor Pair Evaluation on Point Clouds
2020 28th European Signal Processing Conference (EUSIPCO), 2021In recent years, computer vision research has focused on extracting features from 3D data. In this work, we reviewed methods of extracting local features from objects represented in the form of point clouds. The goal of the work was to make theoretical overview and evaluation of selected point cloud detectors and descriptors.
Paula Stancelova +2 more
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Image feature descriptor based on shape salience points
Neurocomputing, 2013Abstract The work presented in this article aims at shape feature extraction and description. In this paper, we propose a shape-based image retrieval technique using salience points to describe shapes. The saliences of a shape are defined as the higher curvature points along the shape contour.
Glauco Vitor Pedrosa +2 more
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I-BRIEF: A Fast Feature Point Descriptor with More Robust Features
2011 Seventh International Conference on Signal Image Technology & Internet-Based Systems, 2011Famous feature point descriptors such as SIFT and SURF allow reliable real-time matching but at a computational cost that limits the number of points that can be handled on PCs, and even more on less powerful mobile devices. A recently proposed technique called Binary Robust Independent Elementary Features (BRIEF) uses binary string as an efficient ...
Jie Liu, Xiaohui Liang 0001
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ICP registration based on 3D point clouds feature descriptor
Twelfth International Conference on Graphics and Image Processing (ICGIP 2020), 2021Widely used in 3D modeling, reverse engineering and other fields, point cloud registration aims to find the translation and rotation matrix between two point clouds obtained from different perspectives, and thus correctly match the two point clouds.
Ying He, Jun Yang, Zhiheng Li, Bin Liang
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Image Matching Algorithm based on Feature-point and DAISY Descriptor
Journal of Multimedia, 2014Image matching technology is the research foundation of many computer vision problems, and the matching algorithm based on partial features of images is a research focus in this field. In order to overcome the unstable performance of classic SURF algorithm on rotation invariance, an image matching algorithm combined with SURF feature-point and DAISY ...
Li Li, John Zic
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Evaluation of 3D feature descriptors for classification of surface geometries in point clouds
2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012This paper investigates existing methods for 3D point feature description with a special emphasis on their expressiveness of the local surface geometry. We choose three promising descriptors, namely Radius-Based Surface Descriptor (RSD), Principal Curvatures (PC) and Fast Point Feature Histograms (FPFH), and present an approach for each of them to show
Georg Arbeiter +4 more
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GPU-accelerated feature point matching using extended ColourFAST descriptors
2015 International Conference on Image and Vision Computing New Zealand (IVCNZ), 2015A real-time feature point matching algorithm is introduced. It extracts vector-based ColourFAST feature strength and direction measures from the colour channels of the pixels in an image. This information is combined with the relative locations of the feature points to provide frame-by-frame scale and rotation invariant matching.
Eleanor Da Fonseca +2 more
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Aligning Point Clouds with an Effective Local Feature Descriptor
2019Point cloud registration is a crucial step and gaining more importance in many challenging 3D computer vision tasks including 3D reconstruction, autonomous navigation, 3D object recognition and remote sensing. In this work, we proposed a highly discriminative local feature descriptor named Local Point Feature Histogram (LPFH) for 3D point cloud ...
Xialing Feng +3 more
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A modified feature point descriptor based on binary robust independent elementary features
2014 7th International Congress on Image and Signal Processing, 2014We present a modified feature point descriptor (M-BRIEF) based on Binary Robust Independent Elementary Features (BRIEF). BRIEF is much faster both to build and to match than SIFT and SURF, and it yields a better recognition as well. However, the matching results are not robust when the viewpoint changes obviously.
Fengquan Zhang, Feng Ye, Zhitong Su
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