publication . Preprint . 2018

AtlasNet: A Papier-M\^ach\'e Approach to Learning 3D Surface Generation

Groueix, Thibault; Fisher, Matthew; Kim, Vladimir G.; Russell, Bryan C.; Aubry, Mathieu;
Open Access English
  • Published: 14 Feb 2018
Abstract
We introduce a method for learning to generate the surface of 3D shapes. Our approach represents a 3D shape as a collection of parametric surface elements and, in contrast to methods generating voxel grids or point clouds, naturally infers a surface representation of the shape. Beyond its novelty, our new shape generation framework, AtlasNet, comes with significant advantages, such as improved precision and generalization capabilities, and the possibility to generate a shape of arbitrary resolution without memory issues. We demonstrate these benefits and compare to strong baselines on the ShapeNet benchmark for two applications: (i) auto-encoding shapes, and (ii...
Subjects
ACM Computing Classification System: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONComputingMethodologies_COMPUTERGRAPHICS
free text keywords: Computer Science - Computer Vision and Pattern Recognition
Download from
32 references, page 1 of 3

[1] A. Bansal, B. C. Russell, and A. Gupta. Marr revisited: 2d-3d alignment via surface normal prediction. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. [OpenAIRE]

[2] P. J. Besl, N. D. McKay, et al. A method for registration of 3-d shapes. IEEE Transactions on pattern analysis and machine intelligence, 14(2):239-256, 1992. [OpenAIRE]

[3] F. Bogo, J. Romero, M. Loper, and M. J. Black. FAUST: Dataset and evaluation for 3D mesh registration. In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Piscataway, NJ, USA, June 2014. IEEE.

[4] D. Boscaini, J. Masci, E. Rodola, and M. M. Bronstein. Learning shape correspondence with anisotropic convolutional neural networks. NIPS, 2016. [OpenAIRE]

[5] A. X. Chang, T. Funkhouser, L. Guibas, P. Hanrahan, Q. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su, J. Xiao, L. Yi, and F. Yu. ShapeNet: An Information-Rich 3D Model Repository. Technical Report arXiv:1512.03012 [cs.GR], Stanford University - Princeton University - Toyota Technological Institute at Chicago, 2015.

[6] C. B. Choy, D. Xu, J. Gwak, K. Chen, and S. Savarese. 3DR2N2: A unified approach for single and multi-view 3D object reconstruction. In Proceedings of European Conference on Computer Vision (ECCV), 2016.

[7] P. Cignoni, C. Rocchini, and R. Scopigno. Metro: Measuring error on simplified surfaces. In Computer Graphics Forum, volume 17, pages 167-174. Wiley Online Library, 1998. [OpenAIRE]

[8] H. Fan, H. Su, and L. Guibas. A point set generation network for 3D object reconstruction from a single image. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

[9] R. Girdhar, D. Fouhey, M. Rodriguez, and A. Gupta. Learning a predictable and generative vector representation for objects. In Proceedings of European Conference on Computer Vision (ECCV), 2016. [OpenAIRE]

[10] X. Gu, S. Gortler, and H. Hoppe. Geometry images. SIGGRAPH, 2002. [OpenAIRE]

[11] X. Han, Z. Li, H. Huang, E. Kalogerakis, and Y. Yu. Highresolution shape completion using deep neural networks for global structure and local geometry inference. In Proceedings of IEEE International Conference on Computer Vision (ICCV), 2017.

[12] C. Ha¨ne, S. Tulsiani, and J. Malik. Hierarchical surface prediction for 3D object reconstruction. In Proceedings of the International Conference on 3D Vision (3DV), 2017.

[13] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016.

[14] K. Hormann, K. Polthier, and A. Sheffer. Mesh parameterization: Theory and practice. In ACM SIGGRAPH ASIA 2008 Courses, SIGGRAPH Asia '08, pages 12:1-12:87, New York, NY, USA, 2008. ACM.

[15] K. Hornik. Approximation capabilities of multilayer feedforward networks. Neural networks, 4(2):251-257, 1991. [OpenAIRE]

32 references, page 1 of 3
Abstract
We introduce a method for learning to generate the surface of 3D shapes. Our approach represents a 3D shape as a collection of parametric surface elements and, in contrast to methods generating voxel grids or point clouds, naturally infers a surface representation of the shape. Beyond its novelty, our new shape generation framework, AtlasNet, comes with significant advantages, such as improved precision and generalization capabilities, and the possibility to generate a shape of arbitrary resolution without memory issues. We demonstrate these benefits and compare to strong baselines on the ShapeNet benchmark for two applications: (i) auto-encoding shapes, and (ii...
Subjects
ACM Computing Classification System: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONComputingMethodologies_COMPUTERGRAPHICS
free text keywords: Computer Science - Computer Vision and Pattern Recognition
Download from
32 references, page 1 of 3

[1] A. Bansal, B. C. Russell, and A. Gupta. Marr revisited: 2d-3d alignment via surface normal prediction. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. [OpenAIRE]

[2] P. J. Besl, N. D. McKay, et al. A method for registration of 3-d shapes. IEEE Transactions on pattern analysis and machine intelligence, 14(2):239-256, 1992. [OpenAIRE]

[3] F. Bogo, J. Romero, M. Loper, and M. J. Black. FAUST: Dataset and evaluation for 3D mesh registration. In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Piscataway, NJ, USA, June 2014. IEEE.

[4] D. Boscaini, J. Masci, E. Rodola, and M. M. Bronstein. Learning shape correspondence with anisotropic convolutional neural networks. NIPS, 2016. [OpenAIRE]

[5] A. X. Chang, T. Funkhouser, L. Guibas, P. Hanrahan, Q. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su, J. Xiao, L. Yi, and F. Yu. ShapeNet: An Information-Rich 3D Model Repository. Technical Report arXiv:1512.03012 [cs.GR], Stanford University - Princeton University - Toyota Technological Institute at Chicago, 2015.

[6] C. B. Choy, D. Xu, J. Gwak, K. Chen, and S. Savarese. 3DR2N2: A unified approach for single and multi-view 3D object reconstruction. In Proceedings of European Conference on Computer Vision (ECCV), 2016.

[7] P. Cignoni, C. Rocchini, and R. Scopigno. Metro: Measuring error on simplified surfaces. In Computer Graphics Forum, volume 17, pages 167-174. Wiley Online Library, 1998. [OpenAIRE]

[8] H. Fan, H. Su, and L. Guibas. A point set generation network for 3D object reconstruction from a single image. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

[9] R. Girdhar, D. Fouhey, M. Rodriguez, and A. Gupta. Learning a predictable and generative vector representation for objects. In Proceedings of European Conference on Computer Vision (ECCV), 2016. [OpenAIRE]

[10] X. Gu, S. Gortler, and H. Hoppe. Geometry images. SIGGRAPH, 2002. [OpenAIRE]

[11] X. Han, Z. Li, H. Huang, E. Kalogerakis, and Y. Yu. Highresolution shape completion using deep neural networks for global structure and local geometry inference. In Proceedings of IEEE International Conference on Computer Vision (ICCV), 2017.

[12] C. Ha¨ne, S. Tulsiani, and J. Malik. Hierarchical surface prediction for 3D object reconstruction. In Proceedings of the International Conference on 3D Vision (3DV), 2017.

[13] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016.

[14] K. Hormann, K. Polthier, and A. Sheffer. Mesh parameterization: Theory and practice. In ACM SIGGRAPH ASIA 2008 Courses, SIGGRAPH Asia '08, pages 12:1-12:87, New York, NY, USA, 2008. ACM.

[15] K. Hornik. Approximation capabilities of multilayer feedforward networks. Neural networks, 4(2):251-257, 1991. [OpenAIRE]

32 references, page 1 of 3
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