中圖分類號： TP391
文獻(xiàn)標(biāo)識碼： A
DOI： 10.19358/j.issn.2096-5133.2021.05.010
引用格式： 胡茂林，李金龍，胡濤. 基于距離正則化的單視圖三維重建[J].信息技術(shù)與網(wǎng)絡(luò)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