SM-NET: Reconstructing 3D Structured Mesh Models from Single Real-World Image

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dc.contributor.authorYu, Yueen_US
dc.contributor.authorLi, Yingen_US
dc.contributor.authorZhang, Jing-Yuen_US
dc.contributor.authorYang, Yueen_US
dc.contributor.editorLee, Sung-Hee and Zollmann, Stefanie and Okabe, Makoto and Wünsche, Burkharden_US
dc.date.accessioned2021-10-14T10:05:43Z
dc.date.available2021-10-14T10:05:43Z
dc.date.issued2021
dc.description.abstractImage-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.en_US
dc.description.sectionheadersNeural Rendering and 3D Models
dc.description.seriesinformationPacific Graphics Short Papers, Posters, and Work-in-Progress Papers
dc.identifier.doi10.2312/pg.20211388
dc.identifier.isbn978-3-03868-162-5
dc.identifier.pages55-60
dc.identifier.urihttps://doi.org/10.2312/pg.20211388
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/pg20211388
dc.publisherThe Eurographics Associationen_US
dc.subjectComputing methodologies
dc.subjectReconstruction
dc.subjectMesh models
dc.subjectNeural networks
dc.titleSM-NET: Reconstructing 3D Structured Mesh Models from Single Real-World Imageen_US
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