基于三元组网络的单图三维模型检索

随着媒体数据的多样化发展，联合图像与三维模型的跨域检索成为三维模型检索问题的一个新挑战。针对图像与三维模型差异大、难匹配问题，提出了一种基于三元组网络的跨域数据检索方法。以端到端的方式构建真实图像与三维模型的特征联合嵌入空间，通过特征间的距离度量不同模态数据之间的相似性，实现从单张图像检索相似的三维模型。为了提高跨域检索准确度，将三维模型用一组顺序视图表示，结合门控循环单元（GRU）聚合视图级特征，同时引入注意力机制提取图像特征，缩小真实图像与投影视图间的语义差异。实验结果表明:相比于同类方法，所提方法在两个跨域数据集上的检索平均准确率至少提升2.98%~3.05%。 

Monocular image based 3D model retrieval using triplet network

With the diversified development of media data, the cross-domain retrieval between images and 3D models becomes a new challenge for 3D model retrieval. In view that images and 3D models are extremely different and hard to match, a cross-domain retrieval algorithm based on triple network is proposed to construct a joint embedding space for real images and 3D shapes in an end-to-end manner. Then the similarity between different modal data could be effectively computed by the distance in the space, leading to accurate retrieval of similar 3D models from single image. In order to improve the accuracy of cross-domain retrieval, the 3D model was represented by a set of sequential views, and the Gate Recurrent Unit (GRU) was utilized for view-level features to generate the global feature. In addition, an attention mechanism was introduced to extract image features and bridge the semantic gaps between the real image and the rendered 3D views. Experimental results show that the mean average precision can be improved by at least 2.98%-3.05% on two cross-domain datasets compared with other similar algorithms.