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

作者：Li, Yangyang; Shi, Licheng; Wan, Weibing; Zhao, Qunfei^*

来源：Journal of Shanghai Jiaotong University, 2018, 52(1): 7-12.

DOI：10.16183/j.cnki.jsjtu.2018.01.002

• 3-D object detections
• 3D object
• Convolutional neural network
• Convolutional Neural Networks (CNN)
• K-Means clustering algorithm
• Random sample consensus
• Recognition rates
• Service robots

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.

• 出版日期2018
• 单位上海交通大学