A Convolutional Neural Network-Based Method for 3D Object Detection

doi:10.16183/j.cnki.jsjtu.2018.01.002

Abstract

Abstract: We used random sample consensus and distance cluster to segment object instead of sliding windows. In recognition step, we designed a new algorithm to extract point cloud feature. Firstly, the point cloud of objects was converted to depth map, then k-Means is applied to learn features from random patches. The learned feature can be used as the convolutional neural network (CNN) filters and convolved over the input image to extract convolutional feature. The presented method was tested by using two public datasets. The results showed that feature learned by single layer CNN can achieve higher recognition rate than artificially designed feature.

Key words: service robot, 3D object, detection, k-Means clustering algorithm, convolutional neural network (CNN)

CLC Number:

TP 24

LI Yangyang,SHI Licheng,WAN Weibing,ZHAO Qunfei. A Convolutional Neural Network-Based Method for 3D Object Detection[J]. Journal of Shanghai Jiaotong University, 2018, 52(1): 7-12.

References

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