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首页> 外文期刊>IEEE transactions on visualization and computer graphics >Viewpoint Assessment and Recommendation for Photographing Architectures
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Viewpoint Assessment and Recommendation for Photographing Architectures

机译：摄影构架的观点评估和建议

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This paper studies the problem of how to assess the quality of photographing viewpoints and how to choose good viewpoints for taking photographs of architectures. We achieve this by learning from photographs of world famous landmarks that are available on the Internet and their viewpoint quality ranked by online user annotation. Unlike previous efforts devoted to photo quality assessment which mainly rely on 2D image features, we show in this paper combining 2D image features extracted from images with 3D geometric features computed on the 3D models can result in more reliable evaluation of viewpoint quality. Specifically, we collect a set of photographs for each of 15 world famous architectures as well as their 3D models from the Internet. Viewpoint recovery for images is carried out through an image-model registration process, after which a newly proposed viewpoint clustering strategy is exploited to validate users' viewpoint preferences when photographing landmarks. Finally, we extract a number of 2D and 3D features for each image based on multiple visual and geometric cues and perform viewpoint recommendation by learning from both 2D and 3D features using a specifically designed SVM-2K multi-view learner, achieving superior performance over using solely 2D or 3D features. We show the effectiveness of the proposed approach through extensive experiments. The experiments also demonstrate that our system can be used to recommend viewpoints for rendering textured 3D models of buildings for the use of architectural design, in addition to viewpoint evaluation of photographs and recommendation of viewpoints for photographing architectures in practice.
机译：本文研究了如何评估摄影观点的质量以及如何为建筑摄影选择好的观点的问题。我们通过从互联网上可获得的世界著名地标的照片中学习，并通过在线用户注释对它们的视点质量进行排名，来实现这一目标。与先前致力于照片质量评估的工作主要依靠2D图像特征不同，我们在本文中展示了将从图像中提取的2D图像特征与在3D模型上计算出的3D几何特征相结合可以产生更可靠的视点质量评估。具体来说，我们从Internet上收集了15种世界闻名的体系结构及其3D模型的照片集。通过图像模型注册过程执行图像的视点恢复，此后，在拍摄地标时，采用了新提出的视点聚类策略来验证用户的视点偏好。最后，我们基于多个视觉和几何线索为每个图像提取许多2D和3D特征，并通过使用专门设计的SVM-2K多视图学习器从2D和3D特征中学习来执行视点推荐，与仅2D或3D功能。我们通过广泛的实验证明了该方法的有效性。实验还证明，除了对照片的视点评估和在实践中拍摄建筑的视点的推荐之外，我们的系统还可用于推荐用于渲染建筑物纹理3D模型以供建筑设计使用的视点。

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