Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (10): 145-150.doi: 10.11896/jsjkx.190900172

• Computer Graphics & Multimedia • Previous Articles     Next Articles

3D Object Detection Algorithm Based on Two-stage Network

SHEN Qi1, CHEN Yi-lun2, LIU Shu3, LIU Li-gang1   

  1. 1 School of Mathematical Sciences,University of Science and Technology of China,Hefei 230026,China
    2 Department of Computer Science,and Engineering,The Chinese University of Hong Kong,Hong Kong 999077,China
    3 Tencent Holdings Ltd.,Shenzhen,Guangdong 518057,China
  • Received:2019-09-25 Revised:2019-11-25 Online:2020-10-15 Published:2020-10-16
  • About author:SHEN Qi,born in 1996,postgraduate.Her main research interests include object detection and so on.
    LIU Li-gang,born in 1975,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include computer graphics and so on.

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Abstract: This paper proposes a 3D object detection algorithm,named VoxelRCNN,on the basis of LIDAR point cloud.This algorithm is based on VoxelNet 3D object detection network algorithm,and the idea of RCNN algorithm is applied to 3D object detection from 2D object detection.The VoxelRCNN algorithm is composed of two stages.Stage-1 aims to extract the information of candidate region box with the regional proposal network,and stage-2 aims to refine the object detection box extracted in stage-1,to obtain more accurate detection results.The stage-1 network voxelizes the point cloud of the whole scene,extracts the features of each voxel block as the input of the convolutional neural network,and obtains the final characteristic map through the convolutional neural network calculation.Then,the enveloping box information is learnt by regression according to the feature map.In stage-2,on the basis of the candidate region information and feature information extracted in stage-1,equivalent feature information is obtained by pooling,and returning to learning bounding box information again.Experimental results on KITTI dataset show that the proposed network structure performs well.

Key words: 3D object detection, Convolutional neural network, Kitti dataset, Region proposal network, Voxelization

CLC Number: 

  • TP391.41
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