An Improved Local Descriptor based Object Recognition in Cluttered 3D Point Clouds

Xiaoni Liu, Yinan Lu, Tieru Wu, Tianwen Yuan


Object recognition in three-dimensional point clouds is a new research topic in the field of computer vision. Numerous nuisances, such as noise, a varying density, and occlusion greatly increase the difficulty of 3D object recognition. An improved local feature descriptor is proposed to address these problems in this paper. At each feature point, a local reference frame is established by calculating a scatter matrix based on the geometric center and the weighted point-cloud density of its neighborhood, and an improved normal vector estimation method is used to generate a new signature of histograms of orientations (SHOT) local-feature descriptor. The geometric consistency and iterative closest point method realize 3D model recognition in the point-cloud scenes. The experimental results show that the proposed SHOT feature-extraction algorithm has high robustness and descriptiveness in the object recognition of 3D local descriptors in cluttered point-cloud scenes.


3D point cloud, local feature, object recognition, noise, density variation

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3D Keypoint Detection Benchmark;

The Stanford 3D Scanning Repository;

SHOT: Unique Signatures of Histograms for Local Surface Description;


Copyright (c) 2018 Xiaoni Liu, Yinan Lu, Tieru Wu, Tianwen Yuan

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