IV-Net: single-view 3D volume reconstruction by fusing features of image and recovered volume

被引:0
|
作者
Beibei Sun
Ping Jiang
Dali Kong
Ting Shen
机构
[1] Hefei University of Technology,School of Mathematics
来源
The Visual Computer | 2023年 / 39卷
关键词
Single-view 3D reconstruction; Multi-scale convolution; Deep learning; Residual convolutional neural network;
D O I
暂无
中图分类号
学科分类号
摘要
Single-view 3D reconstruction aims to recover the 3D shape from one image of an object and has attracted increasingly attention in recent years. Mostly, previous works are devoted to learning a mapping from 2 to 3D, and lack of spatial information of objects will cause inaccurate reconstruction on the details of objects. To address this issue, for single-view 3D reconstruction, we propose a novel voxel-based network by fusing features of image and recovered volume, named IV-Net. By a pre-trained baseline, it achieves image feature and a coarse volume from each image input, where the recovered volume contains spatial semantic information. Specially, the multi-scale convolutional block is designed to improve 2D encoder by extracting multi-scale image information. To recover more accurate shape and details of the object, an IV refiner is further used to reconstruct the final volume. We conduct experimental evaluations on both synthetic ShapeNet dataset and real-world Pix3D dataset, and results of comparative experiments indicate that our IV-Net outperforms state-of-the-art approaches about accuracy and parameters.
引用
收藏
页码:6237 / 6247
页数:10
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