The performances of generative adversarial network (GAN) and autoencoder (AE) models on images have been gathering a great deal of interest in terms of transferring them to three-dimensional (3D) domain. In this study, single image to object reconstruction problem was focused by presenting a novel 2D-to-3D AE model inspired by the recent improvements. To benefit from middle-level features, a model with skip connections was constructed by transferring 2D features to 3D domain. Moreover, the authors considered class-awareness for obtaining a category-agnostic model using limited class-annotations. Apart from recent 3D reconstruction models, they adapted the intersection-over-union score based objective, which is used in the object segmentation model, for improving reconstruction performance. With all these contributions, they call their model as skipped volumetric class-aware AE (SkipVCAE). According to experimental studies, proposed model obtained higher scores than the state-of-the-art model given. The results have proven its performance as a category-specific and category-agnostic model together owing to its class-aware nature. In the further analysis, it was seen that presented model yielded satisfactory results on a single image to object modelling compared to its multi-view version thanks to class-awareness.

中文翻译：

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可识别类的单图像到3D对象的翻译自动编码器

在图像上生成对抗网络（GAN）和自动编码器（AE）模型的性能已经引起了人们极大的兴趣，他们将它们转移到三维（3D）域中。在这项研究中，单一图像到对象的重建问题的重点是提出一种受最新改进启发的新型2D到3D AE模型。为了从中级功能中受益，通过将2D功能部件转移到3D域来构建具有跳过连接的模型。此外，作者考虑了使用有限的类注释来获得类不可知模型的类意识。除了最近的3D重建模型外，他们还改编了用于对象分割模型的基于交集的基于分数的物镜，以提高重建性能。有了这些贡献，他们称其模型为跳过的体积类感知AE（SkipVCAE）。根据实验研究，提出的模型获得的分数比给定的最新模型更高。由于其具有类感知的特性，因此结果证明了其作为特定于类别和与类别无关的模型的性能。在进一步的分析中，由于类意识，与多视图版本相比，提出的模型在单个图像到对象建模上产生了令人满意的结果。