高级检索

面向三维模型草图检索的三元层次度量网络

Triplet Hierarchical Metric Network for Sketch-Based 3D Shape Retrieval

  • 摘要: 针对基于草图的三维模型检索仍然存在将草图视作普通图像忽略其特有的稀疏性, 以及对草图和三维模型的类内差异性重视不足, 从而影响检索性能的问题, 提出一种面向三维模型草图检索的三元层次度量网络. 首先引入笔画点序列分支构建三元组网络结构, 实现对草图数据的信息增强; 然后通过多层次联合损失对网络进行域内域间跨域的全面约束, 使得网络学习到同时体现数据的单域类内差异和域间关系的表示特征, 有效的提升网络的检索性能. 实验结果表明, 所提网络在2个公开数据集SHREC2013和SHREC2014上的平均检索精度均值分别为87.7%和83.3%, 比先进工作(相同基网)分别提升0.5%和1.5%以上.

     

    Abstract: A triplet hierarchical metric network for 3D model sketch retrieval is proposed to address the problem that sketches are treated as ordinary images and their unique sparsity is ignored, and the intra-class differences between sketches and 3D models are not given enough attention, which affects the retrieval performance. Then, the network is fully constrained by multi-level joint loss across domains, so that the network learns to represent both single-domain intra-class differences and inter-domain relationships, which effectively improves the retrieval performance of the network. The experimental results show that the average retrieval accuracy of the proposed network on two publicly available datasets SHREC2013 and SHREC2014 is 87.7% and 83.3%, respectively, which is more than 0.5% and 1.5% better than the advanced work (the same base-net).

     

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