Semantics-Driven Approach for Automatic Selection of Best Views of 3D Shapes
Semantics-Driven Approach for Automatic Selection of Best Views of 3D Shapes
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DOI：
10.2312/3dor/3dor10/015-022
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发表时间：
2010-05
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影响因子：
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通讯作者：
Hamid Laga
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文献类型：
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作者：
Hamid Laga
我们引入了一个新的框架，用于自动选择 3D 模型的最佳视图。该方法基于这样的假设：属于同一类形状的模型具有相同的显着特征，这些特征可以将它们与其他类的模型区分开来。主要问题是学习这些功能。我们提出了一种数据驱动方法，其中最佳视图选择问题被表述为分类和特征选择问题；首先，使用一组基于视图的描述符来描述 3D 模型，每个描述符都是从不同的视点计算的。然后，以监督方式在属于多个形状类别的 3D 模型集合上训练分类器。分类器学习最大化同一类形状之间相似性的 2D 视图集，以及区分不同类形状的视图。我们使用光场 (LFD) 描述符和普林斯顿形状基准进行的实验证明了该方法的性能及其对 3D 数据集的分类和在线可视化浏览的适用性。
We introduce a new framework for the automatic selection of the best views of 3D models. The approach is based on the assumption that models belonging to the same class of shapes share the same salient features that discriminate them from the models of other classes. The main issue is learning these features. We propose a datadriven approach where the best view selection problem is formulated as a classification and feature selection problem; First a 3D model is described with a set of view-based descriptors, each one computed from a different viewpoint. Then a classifier is trained, in a supervised manner, on a collection of 3D models belonging to several shape categories. The classifier learns the set of 2D views that maximize the similarity between shapes of the same class and also the views that discriminate shapes of different classes. Our experiments using the LightField (LFD) descriptors and the Princeton Shape Benchmark demonstrate the performance of the approach and its suitability for classification and online visual browsing of 3D data collections.