Yu, YueLi, YingZhang, Jing-YuYang, YueLee, Sung-Hee and Zollmann, Stefanie and Okabe, Makoto and Wünsche, Burkhard2021-10-142021-10-142021978-3-03868-162-5https://doi.org/10.2312/pg.20211388https://diglib.eg.org:443/handle/10.2312/pg20211388Image-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.Computing methodologiesReconstructionMesh modelsNeural networks10.2312/pg.2021138855-60