Multi-View Convolutional Neural Networks for 3D Shape Recognition

Articles citing Multi-View Convolutional Neural Networks for 3D Shape Recognition

Title / Author	Year
Pointnet: Deep learning on point sets for 3d classification and segmentation CR Qi, H Su, K Mo, LJ Guibas Proceedings of the IEEE Conference on Computer Vision and Pattern …	2017
Pointnet++: Deep hierarchical feature learning on point sets in a metric space CR Qi, L Yi, H Su, LJ Guibas Advances in neural information processing systems, 5099-5108	2017
Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling J Wu, C Zhang, T Xue, B Freeman, J Tenenbaum Advances in neural information processing systems, 82-90	2016
Geometric deep learning: going beyond euclidean data MM Bronstein, J Bruna, Y LeCun, A Szlam, P Vandergheynst IEEE Signal Processing Magazine 34 (4), 18-42	2017
Volumetric and multi-view cnns for object classification on 3d data CR Qi, H Su, M Nießner, A Dai, M Yan, LJ Guibas Proceedings of the IEEE conference on computer vision and pattern …	2016
Multi-view 3d object detection network for autonomous driving X Chen, H Ma, J Wan, B Li, T Xia Proceedings of the IEEE Conference on Computer Vision and Pattern …	2017
Octnet: Learning deep 3d representations at high resolutions G Riegler, A Osman Ulusoy, A Geiger Proceedings of the IEEE Conference on Computer Vision and Pattern …	2017
Geometric deep learning on graphs and manifolds using mixture model cnns F Monti, D Boscaini, J Masci, E Rodola, J Svoboda, MM Bronstein Proceedings of the IEEE Conference on Computer Vision and Pattern …	2017
Scannet: Richly-annotated 3d reconstructions of indoor scenes A Dai, AX Chang, M Savva, M Halber, T Funkhouser, M Nießner Proceedings of the IEEE Conference on Computer Vision and Pattern …	2017
Deep sliding shapes for amodal 3d object detection in rgb-d images S Song, J Xiao Proceedings of the IEEE Conference on Computer Vision and Pattern …	2016
Geodesic convolutional neural networks on riemannian manifolds J Masci, D Boscaini, M Bronstein, P Vandergheynst Proceedings of the IEEE international conference on computer vision …	2015
Deep sets M Zaheer, S Kottur, S Ravanbakhsh, B Poczos, RR Salakhutdinov, ... Advances in neural information processing systems, 3391-3401	2017
Learning a predictable and generative vector representation for objects R Girdhar, DF Fouhey, M Rodriguez, A Gupta European Conference on Computer Vision, 484-499	2016
Perspective transformer nets: Learning single-view 3d object reconstruction without 3d supervision X Yan, J Yang, E Yumer, Y Guo, H Lee Advances in Neural Information Processing Systems, 1696-1704	2016
Escape from cells: Deep kd-networks for the recognition of 3d point cloud models R Klokov, V Lempitsky Proceedings of the IEEE International Conference on Computer Vision, 863-872	2017
Do we really need to collect millions of faces for effective face recognition? I Masi, AT Trần, T Hassner, JT Leksut, G Medioni European Conference on Computer Vision, 579-596	2016
O-cnn: Octree-based convolutional neural networks for 3d shape analysis PS Wang, Y Liu, YX Guo, CY Sun, X Tong ACM Transactions on Graphics (TOG) 36 (4), 72	2017
Learning shape correspondence with anisotropic convolutional neural networks D Boscaini, J Masci, E Rodolà, M Bronstein Advances in Neural Information Processing Systems, 3189-3197	2016
Frustum pointnets for 3d object detection from rgb-d data CR Qi, W Liu, C Wu, H Su, LJ Guibas Proceedings of the IEEE Conference on Computer Vision and Pattern …	2018
Unsupervised learning of 3d structure from images DJ Rezende, SMA Eslami, S Mohamed, P Battaglia, M Jaderberg, ... Advances in Neural Information Processing Systems, 4996-5004	2016

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