Learning best views of 3D shapes from sketch contour
Learning best views of 3D shapes from sketch contour
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DOI：
10.1007/s00371-015-1091-1
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发表时间：
2015-04
期刊：
影响因子：
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通讯作者：
Long Zhao;Shuang Liang;Jinyuan Jia;Yichen Wei
中科院分区：
文献类型：
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作者：
Long Zhao;Shuang Liang;Jinyuan Jia;Yichen Wei
在本文中，我们介绍了一种新的基于学习的方法，利用新的先验知识自动选择3D形状的最佳视图。我们认为，如果人们通常从3D形状中画出形状，那么3D形状的观点是合理的。从相关数据集中收集的手绘草图用于模拟这一概念。我们通过考虑草图和视点的轮廓的上下文信息来揭示它们之间的联系。此外，提出了一个学习框架来推广这种联系，目的是学习不同类型的3D形状的自动最佳视图选择器。在普林斯顿形状基准数据集上进行了实验，验证了该方法的优越性。实验结果表明，与现有的形状检索方法相比，该方法不仅具有较强的鲁棒性，而且在形状检索任务中具有较高的效率。
In this paper, we introduce a novel learning-based approach to automatically select the best views of 3D shapes using a new prior. We think that a viewpoint of the 3D shape is reasonable if a human usually draws the shape from it. Hand-drawn sketches collected from relevant datasets are used to model this concept. We reveal the connection between sketches and viewpoints by taking context information of their contours into account. Furthermore, a learning framework is proposed to generalize this connection which aims to learn an automatic best view selector for different kinds of 3D shapes. Experiments on the Princeton Shape Benchmark dataset are conducted to demonstrate the superiority of our approach. The results show that compared with other state-of-the-art methods, our approach is not only robust but also efficient when applied to shape retrieval tasks.