从2D图像到3D图像的演化

V. S, S. M
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引用次数: 0

摘要

提出一个模型,该模型将通过使用无监督技术学习可变形对象类别来重塑原始3D特定单视图图像。使用卷积自编码器,每个原始图像将被作为输入,然后通过考虑诸如深度,反照率,视点和特定输入的照明等因素来压缩。自动编码器基本上用于以无监督的方式进行有效的数据编码。基于对象的对称结构,这些组件可以在没有任何标签或监督的情况下解除纠缠。这个模型展示了关于照明的推理,它允许我们利用潜在的物体对称性,即使外观由于阴影而不对称。此外,该模型通过预测对称概率映射,与模型的其他组件端到端学习可能(但不确定)对称的对象。该模型可以非常准确地从单视图图像中恢复人脸,猫脸和汽车的3D形状,而无需任何监督或先前的形状模型。在基准测试中,与在2D图像对应级别使用监督的另一种方法相比,模型将被证明具有更高的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evolution of 3D images from 2D images
To present a model, which will reshape the raw 3D specific single-view images by learning the deformable object categories using unsupervised techniques. Using Convolutional autoencoders, each raw image will be taken as input, then this will be compressed by considering factors such as depth, albedo, viewpoint and illumination of a particular input. Autoencoders are basically used for efficient data codings in an unsupervised manner. These components can disentangled without any labels or supervisor based on the symmetric structure of the object. This model shows the reasoning about illumination which allows us to exploit the underlying object symmetry even if the appearance is not symmetric due to shading. Furthermore, this model objects that are probably, but not certainly, symmetric by predicting a symmetry probability map, learned end-to-end with the other components of the model. This model can recover very accurately the 3D shape of human faces, cat faces and cars from single-view images, without any supervision or a prior shape model. On benchmarks, model will be demonstrated with superior accuracy when compared to another method that uses supervision at the level of 2D image correspondences.
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