SM-NET: Reconstructing 3D Structured Mesh Models from Single Real-World Image
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Date
2021
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Image-based 3D structured model reconstruction enables the network to learn the missing information between the dimensions and understand the structure of the 3D model. In this paper, SM-NET is proposed in order to reconstruct 3D structured mesh model based on single real-world image. First, it considers the model as a sequence of parts and designs a shape autoencoder to autoencode 3D model. Second, the network extracts 2.5D information from the real-world image and maps it to the latent space of the shape autoencoder. Finally, both are connected to complete the reconstruction task. Besides, a more reasonable 3D structured model dataset is built to enhance the effect of reconstruction. The experimental results show that we achieve the reconstruction of 3D structured mesh model based on single real-world image, outperforming other approaches.
Description
@inproceedings{10.2312:pg.20211388,
booktitle = {Pacific Graphics Short Papers, Posters, and Work-in-Progress Papers},
editor = {Lee, Sung-Hee and Zollmann, Stefanie and Okabe, Makoto and Wünsche, Burkhard},
title = {{SM-NET: Reconstructing 3D Structured Mesh Models from Single Real-World Image}},
author = {Yu, Yue and Li, Ying and Zhang, Jing-Yu and Yang, Yue},
year = {2021},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-162-5},
DOI = {10.2312/pg.20211388}
}