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http://dx.doi.org/10.7471/ikeee.2022.26.2.160

3D Mesh Reconstruction Technique from Single Image using Deep Learning and Sphere Shape Transformation Method  

Kim, Jeong-Yoon (Dept. Electronic Engineering, Hanbat National University)
Lee, Seung-Ho (Dept. Electronic Engineering, Hanbat National University)
Publication Information
Journal of IKEEE / v.26, no.2, 2022 , pp. 160-168 More about this Journal
Abstract
In this paper, we propose a 3D mesh reconstruction method from a single image using deep learning and a sphere shape transformation method. The proposed method has the following originality that is different from the existing method. First, the position of the vertex of the sphere is modified to be very similar to the 3D point cloud of an object through a deep learning network, unlike the existing method of building edges or faces by connecting nearby points. Because 3D point cloud is used, less memory is required and faster operation is possible because only addition operation is performed between offset value at the vertices of the sphere. Second, the 3D mesh is reconstructed by covering the surface information of the sphere on the modified vertices. Even when the distance between the points of the 3D point cloud created by correcting the position of the vertices of the sphere is not constant, it already has the face information of the sphere called face information of the sphere, which indicates whether the points are connected or not, thereby preventing simplification or loss of expression. can do. In order to evaluate the objective reliability of the proposed method, the experiment was conducted in the same way as in the comparative papers using the ShapeNet dataset, which is an open standard dataset. As a result, the IoU value of the method proposed in this paper was 0.581, and the chamfer distance value was It was calculated as 0.212. The higher the IoU value and the lower the chamfer distance value, the better the results. Therefore, the efficiency of the 3D mesh reconstruction was demonstrated compared to the methods published in other papers.
Keywords
3D Mesh; Reconstruction; Reparameterization Trick; Latent Vector; Deep Learning;
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1 Gokturk, S. Burak, Hakan Yalcin, and Cyrus Bamji. "A time-of-flight depth sensor-system description, issues and solutions," 2004 Conference on Computer Vision and Pattern Recognition Workshop. IEEE, 2004. DOI: 10.1109/CVPR.2004.291   DOI
2 Choy, Christopher B., et al. "3d-r2n2: A unified approach for single and multi-view 3d object reconstruction," European conference on computer vision. Springer, Cham, 2016.
3 KINGMA, Diederik P.; WELLING, Max. "Autoencoding variational bayes," International Conference on Learning Representations (ICLR), 2014.
4 Fan, Haoqiang, Hao Su, and Leonidas J. Guibas. "A point set generation network for 3d object reconstruction from a single image," Proceedings of the IEEE conference on computer vision and pattern recognition. 2017. DOI: 10.48550/arXiv.1612.00603   DOI
5 Mescheder, Lars, et al. "Occupancy networks: Learning 3d reconstruction in function space." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019.
6 Wang, Nanyang, et al. "Pixel2mesh: Generating 3d mesh models from single rgb images." Proceedings of the European Conference on Computer Vision (ECCV). 2018.
7 Groueix, Thibault, et al. "A papier-mache approach to learning 3d surface generation," Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.
8 CIPRESSO, Pietro, et al. "The past, present, and future of virtual and augmented reality research: a network and cluster analysis of the literature," Frontiers in psychology 2086, 2018. DOI: 10.3389/fpsyg.2018.02086   DOI