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首页> 外文期刊>Pattern Analysis and Machine Intelligence, IEEE Transactions on >Linear Local Models for Monocular Reconstruction of Deformable Surfaces
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Linear Local Models for Monocular Reconstruction of Deformable Surfaces

机译：可变形表面单目重构的线性局部模型

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摘要

Recovering the 3D shape of a nonrigid surface from a single viewpoint is known to be both ambiguous and challenging. Resolving the ambiguities typically requires prior knowledge about the most likely deformations that the surface may undergo. It often takes the form of a global deformation model that can be learned from training data. While effective, this approach suffers from the fact that a new model must be learned for each new surface, which means acquiring new training data, and may be impractical. In this paper, we replace the global models by linear local models for surface patches, which can be assembled to represent arbitrary surface shapes as long as they are made of the same material. Not only do they eliminate the need to retrain the model for different surface shapes, they also let us formulate 3D shape reconstruction from correspondences as either an algebraic problem that can be solved in closed form or a convex optimization problem whose solution can be found using standard numerical packages. We present quantitative results on synthetic data, as well as qualitative results on real images.
机译：从单一角度恢复非刚性表面的3D形状是模棱两可且充满挑战的。解决模糊性通常需要事先了解表面可能发生的最可能变形。它通常采用可从训练数据中学习的整体变形模型的形式。这种方法虽然有效，但存在以下缺点：必须为每个新表面学习一个新模型，这意味着获取新的训练数据，并且可能不切实际。在本文中，我们将线性局部模型替换为表面补丁的全局模型，只要它们是由相同的材料制成，就可以组装它们以表示任意的表面形状。他们不仅消除了针对不同表面形状重新训练模型的需要，而且还让我们根据对应关系来制定3D形状重构，可以将其作为可封闭形式求解的代数问题，也可以通过使用标准可找到其解的凸优化问题进行求解。数字包。我们在合成数据上给出定量结果，在真实图像上给出定性结果。

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