Computer Science ›› 2019, Vol. 46 ›› Issue (3): 148-153.doi: 10.11896/j.issn.1002-137X.2019.03.022

• ChinaMM2018 • Previous Articles     Next Articles

3-D Model Retrieval Algorithm Based on Residual Network

LI Yin-min1,2,XUE Kai-xin1,2,GAO Zan1,2,3,XUE Yan-bin1,2,XU Guang-ping1,2,ZHANG Hua1,2   

  1. (Key Laboratory of Computer Vision and System of Ministry of Education,Tianjin University of Technology,Tianjin 300384,China)1
    (Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology,Tianjin University of Technology,Tianjin 300384,China)2
    (Institute of AI,Shandong Computer Center,Qilu University of Technology (Shandong Academy of Science),Jinan 250014,China)3
  • Received:2018-07-11 Revised:2018-09-21 Online:2019-03-15 Published:2019-03-22

Abstract: In recent years,view-based 3D model retrieval has become a key research direction in the field of computer vision.The 3D model retrieval algorithm includes feature extraction and model retrieval where robust features play a decisive rolein retrieval algorithm.Up to now,the traditional hand-crafted features and deep learning features were proposed,but very few people systematically compare them.Therefore,in this work,the performance of different artificial design features and deep learning features was evaluated and analyzed.Based on the premise of full comparison,multiple data sets,multiple evaluation criteria,and different search algorithms were used to conduct experiments.The effects of different layers of deep network on performance were further compared,and a 3D model retrieval algorithm based on residual network was proposed.Several conclusions could be obtained from the experimental results on multiple public datasets.1)When comparing the deep learning features of VGG network and residual network with traditional hand-crafted features,the improvement of comprehensive performance can reaches 3% to 20%.2)Compared with the deep features extracted by VGG network,the comprehensive performance of the residual network is increased by 1% to 5%.3)The performance of different layer features in the VGG network is also different,and the comprehensive performance of the deep and shallow features is increased by 1% to 6%.4)As the depth of the network increase,the overall perfor-mance of the extracted features of the residual network has limited improvement,and is more robust than other contrasting features.

Key words:

第3期李荫民, Deep features, Feature extraction, Hand-crafted features, Residual network, 等:基于残差网络的三维模型检索算法
3D model retrieval

CLC Number: 

  • TP391.4
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