基于视点对齐的跨域三维模型检索

Cross-domain 3D Model Retrieval Based on Viewpoint Alignment

以草图为输入的三维模型检索便于用户表达搜索需求,已成为一个研究热点。但草图只是三维模型一个视点下的粗略描述,具有很高的抽象性。而且草图和三维模型之间存在巨大的域间差异,这导致目前的草图检索系统准确率低。为解决这一问题,提出了一个新的基于草图的三维模型检索方法。该方法根据草图的视点对三维模型投影产生一组二维视图,形成一对多且视点对齐的草图视图训练数据对。然后采用Canny算子提取视图的多层次伪草图,减小视图和草图的域差异,并采用样本选择和数据增强缓解训练数据不均衡的问题。接下来用伪草图和草图训练三元组网络(triplet network),将伪草图和草图映射到公共特征嵌入空间。最后用Triplet提取特征和建立索引,实现草图检索。实验结果表明:在大型公共数据集SHREC’13和SHREC’14的检索精度分别是70.0%和63.6%,有效提高了检索准确率。

3D model retrieval with sketches as input is convenient for users to express their retrieval needs,and has become a research hotspot.However,a sketch which is only the rough description of a 3D model from a single viewpoint has high degree of abstraction.In addition,there are large domain differences between sketches and the 3D models.These lead to low accuracy of the current sketch retrieval systems.To solve these problems,we propose a new sketch-based 3D model retrieval method.In this method,a 3D model is firstly projected to a set of 2D views with aligned viewpoints,thereby obtaining one-to-many sketch-views pairs.Secondly,the Canny edge detection algorithm is used to extract the multi-level pseudo-sketches from each view,which aims at reducing the domain differences between the views and the sketches.Thirdly,sample selection and data enhancement are adopted to alleviate unbalanced training data.Finally,the prepared data are used to train a Triplet Network which maps the pseudo-sketches and sketches to the common feature embedding space.The Triplet Network is finally used to extract features and build indexes for sketch retrieval.Experiments show that the retrieval accuracy of the proposed method on SHREC’13 and SHREC’14 can reach up to 70.0%and 63.6%,respectively.