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Sensors 2014, 14(12), 24156-24173; doi:10.3390/s141224156

Recognizing Objects in 3D Point Clouds with Multi-Scale Local Features

1
College of Electronic Science and Engineering, National University of Defense Technology, Changsha, Hunan 410073, China
2
Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON N2L 3G1, Canada
3
College of Electronics and Information Engineering, Sichuan University, Chengdu 610064, China
*
Author to whom correspondence should be addressed.
Received: 26 August 2014 / Revised: 21 November 2014 / Accepted: 3 December 2014 / Published: 15 December 2014
(This article belongs to the Section Remote Sensors)
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Abstract

Recognizing 3D objects from point clouds in the presence of significant clutter and occlusion is a highly challenging task. In this paper, we present a coarse-to-fine 3D object recognition algorithm. During the phase of offline training, each model is represented with a set of multi-scale local surface features. During the phase of online recognition, a set of keypoints are first detected from each scene. The local surfaces around these keypoints are further encoded with multi-scale feature descriptors. These scene features are then matched against all model features to generate recognition hypotheses, which include model hypotheses and pose hypotheses. Finally, these hypotheses are verified to produce recognition results. The proposed algorithm was tested on two standard datasets, with rigorous comparisons to the state-of-the-art algorithms. Experimental results show that our algorithm was fully automatic and highly effective. It was also very robust to occlusion and clutter. It achieved the best recognition performance on all of these datasets, showing its superiority compared to existing algorithms. View Full-Text
Keywords: object recognition; point cloud; local feature; clutter; occlusion object recognition; point cloud; local feature; clutter; occlusion
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Lu, M.; Guo, Y.; Zhang, J.; Ma, Y.; Lei, Y. Recognizing Objects in 3D Point Clouds with Multi-Scale Local Features. Sensors 2014, 14, 24156-24173.

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