摘要： 针对从一张物体有限的二维RGB图像信息中还原物体的三维形状信息，提出了基于距离正则化的单视图三维重建。利用二维卷积神经网络(Convolutional Neural Network，CNN)设计图像编码器和三维卷积神经网络设计残差块，再利用残差块为基础设计三维形状生成器,即三维残差生成器。给定一张物体的二维RGB图像，首先使用图像编码器提取RGB图像的语义信息；然后，三维形状生成器使用语义信息，恢复RGB图像中物体的三维形状信息。同时，提出了距离规则化损失，在训练过程中，保证三维物体形状重建质量。实验结果显示，本方法在交并比(Intersection over Union，IoU)评价指标上超过了之前最好的方法。

中图分类号： TP391
文献标识码： A
DOI： 10.19358/j.issn.2096-5133.2021.05.010
引用格式： 胡茂林，李金龙，胡涛. 基于距离正则化的单视图三维重建[J].信息技术与网络安全，2021，40(5)：56-61.

Single-view 3D reconstruction based on margin regularization

Abstract： In order to recover the 3D shape of the object in the input image from the limited information of 2D RGB image, this paper proposes a single image 3D reconstruction method based on a margin regularization loss. This paper uses 2D convolutional neural network(CNN) to design an image encoder and employ 3D CNN to design a special residual block, and then uses residual block to design 3D residual generator. Given a 2D RGB image of an object, firstly, we use our designed image encoder to extract the semantic information of the RGB image; then, the 3D residual generator takes the semantic information as input and recover the 3D shape of the object in the RGB image. At the training phase, this paper proposes a distance regularization loss to ensure the quality of 3D object shape reconstruction during the recovering process. Experiment results demonstrate that the proposed method surpasses the previous best method in the metrics of Intersection over Union(IoU).

Key words : 3D reconstruction；convolutional neural networks；residual block；residual network