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

Sensors

Min Lu¹  Yulan Guo¹  Jun Zhang¹  Yanxin Ma¹ 

[1] College of Electronic Science and Engineering, National University of Defense Technology, Changsha, Hunan 410073, China; E-Mails:

关键词: object recognition;    point cloud;    local feature;    clutter;    occlusion;   

DOI  :  10.3390/s141224156

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.