Sketch-based 3D model retrieval utilizing adaptive view clustering and semantic information
Sketch-based 3D model retrieval utilizing adaptive view clustering and semantic information
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DOI：
10.1007/s11042-016-4187-3
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发表时间：
2017-12
影响因子：
3.6
通讯作者：
Bo Li;Yijuan Lu;H. Johan;Ribel Fares
中科院分区：
文献类型：
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作者：
Bo Li;Yijuan Lu;H. Johan;Ribel Fares
在基于手绘草图的三维建模与识别、人机交互、三维动画、游戏设计等应用中，基于手绘草图搜索相关的三维模型既直观又重要。在本文中，我们的目标是在准确性和效率方面显著提高当前基于草图的三维检索性能。利用自适应视图聚类和语义信息，提出了一种基于草图的三维模型检索框架。首先，利用提出的基于视点熵的三维信息复杂度度量来指导三维模型的自适应视图聚类，以入围一组具有代表性的样本视图，用于2D-3D比较。为了弥合查询草图和目标模型之间的差距，我们结合了一种新的基于语义草图的搜索方法，以进一步提高检索性能。在几个最新的基准测试上的实验结果明显地证明了我们在检索性能上的显著改进。
Searching for relevant 3D models based on hand-drawn sketches is both intuitive and important for many applications, such as sketch-based 3D modeling and recognition, human computer interaction, 3D animation, game design, and etc. In this paper, our target is to significantly improve the current sketch-based 3D retrieval performance in terms of both accuracy and efficiency. We propose a new sketch-based 3D model retrieval framework by utilizing adaptive view clustering and semantic information. It first utilizes a proposed viewpoint entropy-based 3D information complexity measurement to guide adaptive view clustering of a 3D model to shortlist a set of representative sample views for 2D-3D comparison. To bridge the gap between the query sketches and the target models, we then incorporate a novel semantic sketch-based search approach to further improve the retrieval performance. Experimental results on several latest benchmarks have evidently demonstrated our significant improvement in retrieval performance.