计算机科学 ›› 2019, Vol. 46 ›› Issue (3): 148-153.doi: 10.11896/j.issn.1002-137X.2019.03.022
李荫民1,2,薛凯心1,2,高赞1,2,3,薛彦兵1,2,徐光平1,2,张桦1,2
LI Yin-min1,2,XUE Kai-xin1,2,GAO Zan1,2,3,XUE Yan-bin1,2,XU Guang-ping1,2,ZHANG Hua1,2
摘要: 近年来,基于视图的3D模型检索已经成为计算机视觉领域的重点研究方向。3D模型检索算法包括特征提取和检索算法两个部分,且鲁棒的特征对于检索算法起着决定性的作用。目前,研究者们已经提出了许多人工设计特征和深度学习特征,但是很少有人比较它们的异同。因此,文中对不同的人工设计特征和深度学习特征的性能进行了评估分析,基于充分对比的前提,采用了多个数据集、多样的评价标准和不同的检索算法进行了实验,并进一步比较了深度网络不同层特征对性能的影响,从而提出了基于残差网络的三维模型检索算法。在多个公开数据集上的实验表明:1)残差网络所提取的深度特征相较于传统特征,综合性能提升了1%~20%;2)与VGG网络所提取的深度特征相比,残差网络的综合性能提升了1%~5%;3)VGG网络中不同层特征的性能也有差异,深层特征与浅层特征相比,综合性能提升了1%~6%;4)随着网络深度的增加,残差网络所提取的特征的综合性能得到了有限提高,且比其他对比特征均更加鲁棒。
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