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首页> 外文期刊>Computer vision and image understanding >A 3D deformable model-based framework for the retrieval of near-isometric flattenable objects using Bag-of-Visual-Words
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A 3D deformable model-based framework for the retrieval of near-isometric flattenable objects using Bag-of-Visual-Words

机译：一个基于3D变形模型的框架，用于使用视觉袋提取近等距的扁平对象

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We introduce a 3D deformable model-based framework for the retrieval of near-isometric flattenable objects using keypoints and BoVW (Bag-of-Visual-Words). By 3D deformable model we mean a texturemapped 3D shape which may deform isometrically. We assume that such a model is available for each object in the database. We exploit the 3D deformable models at the training and the retrieval phases. For our first contribution, we exploit the possibility of generating synthetic data from the 3D deformable models to define a new BoVW model for the database object representation. Our model chooses an optimal per-object representation by maximizing each object’s mean average precision. The maximization is done over multiple candidate representations which are generated using the criteria of keypoint repeatability, weight discriminance and stability. Our second contribution is the use of SfT (Shape-from-Template) to facilitate geometric verification at the retrieval phase, for a few objects hypothesized using the new BoVW model. Existing methods use a rigid model, such as the fundamental matrix, or a simple deformable model based on semi-local constraints. SfT however is a physics-based method which uses an object’s 3D deformable model to reconstruct its isometric 3D deformation from a single input image. The output of SfT thus directly provides a geometric verification score. A byproduct of our work is to extend the scope of SfT. The proposed object retrieval framework is used to provide SfT with a few object hypotheses which may be quickly tested for the 3D deformable object selection. Performance evaluation on synthetic and real images reveals the benefits of our retrieval framework using a database with size varying between 20 and 1000 objects. The use of the new BoVW model and SfT versus the BoVW baseline and a rigid model improves the retrieval performance by 4.2% and 11.3% withp-values of5×10−6and7×10−30respectively.
机译：我们引入了一个基于3D变形模型的框架，该框架使用关键点和BoVW（视觉单词袋）来检索等距的扁平对象。通过3D变形模型，我们是指可以等距变形的纹理贴图3D形状。我们假定这种模型可用于数据库中的每个对象。我们在训练和检索阶段利用3D变形模型。对于我们的第一个贡献，我们利用从3D变形模型生成合成数据的可能性来定义数据库对象表示的新BoVW模型。我们的模型通过最大化每个对象的平均平均精度来选择最佳的每个对象表示形式。最大化是通过使用关键点可重复性，权重判别和稳定性的标准生成的多个候选表示来完成的。对于使用新BoVW模型假设的一些对象，我们的第二个贡献是使用SfT（模板形状）在检索阶段促进几何验证。现有方法使用刚性模型（例如基本矩阵）或基于半局部约束的简单可变形模型。但是，SfT是一种基于物理学的方法，它使用对象的3D变形模型从单个输入图像重建其等距3D变形。因此，SfT的输出直接提供了几何验证分数。我们工作的副产品是扩大SfT的范围。所提出的对象检索框架用于为SfT提供一些对象假设，可以快速测试3D变形对象选择。通过使用大小在20到1000个对象之间变化的数据库，对合成图像和真实图像的性能评估揭示了我们的检索框架的优势。与BoVW基线和刚性模型相比，使用新的BoVW模型和SfT可以将检索性能分别提高4.2％和11.3％，p值分别为5×10-6和7×10-30。

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