The multi-view of an object can be used for 3D reconstruction. The method proposed in this paper generates the left and the top view of a target car through deep learning. The input of the method is only a front view of a 3D car and it isn’t necessary for the depth of the 3D car. Firstly, a rough orthographic views of the 3D car is gotten from an information constraint network which is constructed by considering the structural relation between one view and the other two views. And then the rough orthographic views is transformed into large-pixel block rough view through the nearest interpolation, at the same time, the large-pixel blocks are also migrated to improve the quality of the rough orthographic views. Finally, the generative adversarial network with perception loss is used to enhance the large-pixel block view. In addition, the three views generated by the network can be used to synthesize a 3D point cloud shell.

中文翻译：

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基于单个前视图像的汽车三视图生成

对象的多视图可用于 3D 重建。本文提出的方法通过深度学习生成目标汽车的左视图和顶视图。该方法的输入只是3D汽车的前视图，不需要3D汽车的深度。首先，从考虑一个视图和其他两个视图之间的结构关系构建的信息约束网络中得到3D汽车的粗略正交视图。然后通过最近插值将粗糙的正交视图转化为大像素块的粗糙视图，同时大像素块也被迁移以提高粗糙的正交视图的质量。最后，使用具有感知损失的生成对抗网络来增强大像素块视图。此外，