Marr Revisited: 2D-3D Alignment via Surface Normal Prediction, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. ,
DOI : 10.1109/CVPR.2016.642
URL : http://arxiv.org/pdf/1604.01347
A method for registration of 3-D shapes, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.14, issue.2, pp.239-256, 1992. ,
DOI : 10.1109/34.121791
FAUST: Dataset and Evaluation for 3D Mesh Registration, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014. ,
DOI : 10.1109/CVPR.2014.491
Learning shape correspondence with anisotropic convolutional neural networks, NIPS, 2016. ,
ShapeNet: An Information-Rich 3D Model Repository, 2015. ,
3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction, Proceedings of European Conference on Computer Vision (ECCV), 2016. ,
DOI : 10.1109/WACV.2014.6836101
Metro: Measuring Error on Simplified Surfaces, Computer Graphics Forum, pp.167-174, 1998. ,
DOI : 10.1111/1467-8659.00236
A Point Set Generation Network for 3D Object Reconstruction from a Single Image, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. ,
DOI : 10.1109/CVPR.2017.264
Learning a Predictable and Generative Vector Representation for Objects, Proceedings of European Conference on Computer Vision (ECCV), 2016. ,
DOI : 10.1007/978-3-319-46466-4_22
Geometry images, 2002. ,
DOI : 10.1145/566570.566589
Highresolution shape completion using deep neural networks for global structure and local geometry inference, Proceedings of IEEE International Conference on Computer Vision (ICCV), 2017. ,
DOI : 10.1109/iccv.2017.19
Hierarchical surface prediction for 3D object reconstruction, Proceedings of the International Conference on 3D Vision (3DV), 2017. ,
Deep Residual Learning for Image Recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.770-778, 2016. ,
DOI : 10.1109/CVPR.2016.90
URL : http://arxiv.org/pdf/1512.03385
Mesh parameterization: Theory and practice, ACM SIGGRAPH ASIA 2008 Courses, SIGGRAPH Asia '08, pp.1-12, 2008. ,
DOI : 10.1145/1508044.1508091
URL : https://hal.archives-ouvertes.fr/inria-00186795
Approximation capabilities of multilayer feedforward networks, Neural Networks, vol.4, issue.2, pp.251-257, 1991. ,
DOI : 10.1016/0893-6080(91)90009-T
Image-to-Image Translation with Conditional Adversarial Networks, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), p.3229, 2013. ,
DOI : 10.1109/CVPR.2017.632
URL : http://arxiv.org/pdf/1611.07004
Accelerated quadratic proxy for geometric optimization, ACM Transactions on Graphics, vol.35, issue.4, p.2016 ,
DOI : 10.1145/54852.378522
GRASS, Proc. of SIGGRAPH 2017), p.2017 ,
DOI : 10.1007/978-3-319-46466-4_18
URL : https://hal.archives-ouvertes.fr/hal-01351815
Joint embeddings of shapes and images via CNN image purification, ACM Transactions on Graphics, vol.34, issue.6, p.2015 ,
DOI : 10.1109/CVPR.2010.5540018
Deep Exemplar 2D-3D Detection by Adapting from Real to Rendered Views, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. ,
DOI : 10.1109/CVPR.2016.648
URL : http://arxiv.org/pdf/1512.02497
PointNet: Deep learning on point sets for 3D classification and segmentation, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. ,
PointNet++: Deep hierarchical feature learning on point sets in a metric space, Advances in Neural Information Processing Systems (NIPS), 2017. ,
OctNet- Fusion: Learning depth fusion from data, Proceedings of the International Conference on 3D Vision (3DV), 2017. ,
Deep Learning 3D Shape Surfaces Using Geometry Images, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. ,
DOI : 10.1109/TVCG.2011.171
SurfNet: Generating 3D Shape Surfaces Using Deep Residual Networks, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. ,
DOI : 10.1109/CVPR.2017.91
URL : http://arxiv.org/pdf/1703.04079
Octree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputs, 2017 IEEE International Conference on Computer Vision (ICCV), 2017. ,
DOI : 10.1109/ICCV.2017.230
Learning shape abstractions by assembling volumetric primitives. arXiv preprint, 2016. ,
DOI : 10.1109/cvpr.2017.160
URL : http://arxiv.org/pdf/1612.00404
O-CNN, ACM Transactions on Graphics, vol.36, issue.4, p.2017 ,
DOI : 10.1109/TVCG.2010.75
URL : http://arxiv.org/pdf/1712.01537
3d shapenets: A deep representation for volumetric shapes, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.1912-1920, 2015. ,
Unpaired image-toimage translation using cycle-consistent adversarial networks, Proceedings of IEEE International Conference on Computer Vision (ICCV), 2017. ,
DOI : 10.1109/iccv.2017.244