3D Correspondences
by Local Feature Matching
Guy Berdugo, Guy Gilboa
Electrical Engineering Department,
Technion – Israel Institute of Technology, Haifa, Israel
3D Matches Example
Abstract:
There is a growing trend today of incorporating 3D sensors within mobile devices. The heavy power limitations naturally lead to a considerable decrease in the quality of point cloud data. This requires highly robust algorithms in order to produce reliable descriptors. 3D descriptors are essential in solving problems such as point cloud registration, object reconstruction, recognition and tracking.
In this work we propose a new local descriptor, Histogram of Relative Angles (HoRA), which is designed to perform robustly for various data degradations of noise and artifacts. The method is simple in its logic and ecient computationally. The descriptor is based on two steps, computation of a local reference frame (LRF) and relative angles. We propose an improvement to existing state-of-the-art LRF computation algorithm which yields a notable improvement in it repeatability rate for various degradations. The relative angles are shown to be highly descriptive geometric features, yet more robust than surface normal estimation or point distribution, commonly used in most recent state-of-the-art methods (SHOT, USC, Spin Image, ThrIFT, RoPS). We show that HoRA can cope well with noise degradation and low resolution eects. Comprehensive experiments on various benchmarks demonstrate the clear superiority of this approach for moderate-to-low quality input over numerous state of the art algorithms
HoRA feature extraction illustration
Algorithm’s flowchart: 3D surface matching
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including 3D CV Lab dataset
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