论文标题
规范3D畸形图：统一的参数和非参数方法，用于密集的弱监督类别重建
Canonical 3D Deformer Maps: Unifying parametric and non-parametric methods for dense weakly-supervised category reconstruction
论文作者
论文摘要
我们提出了规范的3D变形映射，这是一个共同对象类别的3D形状的新表示形式，可以从独立对象的2D图像集合中学到。我们的方法以一种新颖的方式建立在参数变形模型，非参数3D重建和规范嵌入的概念上，结合了他们的个体优势。特别是，它学会了将每个图像像素与相应的3D对象点的变形模型相关联，该模型是规范的，即与该点的点的身份固有的，并在类别的对象上共享。结果是一种方法，即仅在训练时仅稀疏的2D监督，可以在测试时重建对象的3D形状和对象的纹理，同时在对象实例之间建立有意义的密集对应关系。它还实现了最先进的结果，从而在面孔，汽车和鸟类的公共野外数据集上重建了密集的3D重建。
We propose the Canonical 3D Deformer Map, a new representation of the 3D shape of common object categories that can be learned from a collection of 2D images of independent objects. Our method builds in a novel way on concepts from parametric deformation models, non-parametric 3D reconstruction, and canonical embeddings, combining their individual advantages. In particular, it learns to associate each image pixel with a deformation model of the corresponding 3D object point which is canonical, i.e. intrinsic to the identity of the point and shared across objects of the category. The result is a method that, given only sparse 2D supervision at training time, can, at test time, reconstruct the 3D shape and texture of objects from single views, while establishing meaningful dense correspondences between object instances. It also achieves state-of-the-art results in dense 3D reconstruction on public in-the-wild datasets of faces, cars, and birds.
