Fetching the paper…
Reading the bibliography…
Mesh models are a promising approach for encoding the structure of 3D objects.
Parametric correspondence and chamfer matching: two new techniques for image matching
Barrow, H., Tenenbaum, J., Bolles, R., and Wolf, H · 1977
Earlier work this paper cites.
A general framework for adaptive processing of data structures
Frasconi, P., Gori, M., and Sperduti, A · 1998
Earlier work this paper cites.
Distance between point and triangle in 3d
Eberly, D · 1999
Earlier work this paper cites.
Learning to reconstruct shapes from unseen classes
Zhang, X., Zhang, Z., Zhang, C., Tenenbaum, J., Freeman, B., and Wu, J · 1999
Earlier work this paper cites.
Shape distributions
Osada, R., Funkhouser, T., Chazelle, B., and Dobkin, D · 2002
Earlier work this paper cites.
A new model for learning in graph domains
Gori, M., Monfardini, G., and Scarselli, F · 2005
Earlier work this paper cites.
The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., and Monfardini, G · 2009
Earlier work this paper cites.
Rectified linear units improve restricted boltzmann machines
Nair, V. and Hinton, G. E · 2010
Earlier work this paper cites.
Multi-column deep neural networks for image classification
Ciregan, D., Meier, U., and Schmidhuber, J · 2012
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
Earlier work this paper cites.
Spectral networks and locally connected networks on graphs
Bruna, J., Zaremba, W., Szlam, A., and Lecun, Y · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
Earlier work this paper cites.
Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D · 2014
Earlier work this paper cites.
Shapenet: An information-rich 3d model repository
Chang, A. X., Funkhouser, T., Guibas, L., Hanrahan, P., Huang, Q., Li, Z., Savarese, S., Savva, M., Song, S., Su, H., et al · 2015
Earlier work this paper cites.
Fast and accurate deep network learning by exponential linear units (elus)
Clevert, D.-A., Unterthiner, T., and Hochreiter, S · 2015
Earlier work this paper cites.
Convolutional networks on graphs for learning molecular fingerprints
Duvenaud, D., Maclaurin, D., Aguilera-Iparraguirre, J., Gómez-Bombarelli, R., Hirzel, T., Aspuru-Guzik, A., and Adams, R. P · 2015
Earlier work this paper cites.
Deep convolutional networks on graph-structured data
Henaff, M., Bruna, J., and LeCun, Y · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
Earlier work this paper cites.
Category-specific object reconstruction from a single image
Kar, A., Tulsiani, S., Carreira, J., and Malik, J · 2015
Earlier work this paper cites.
3d-r2n2: A unified approach for single and multi-view 3d object reconstruction
Choy, C. B., Xu, D., Gwak, J., Chen, K., and Savarese, S · 2016
Cited alongside, same era.
Convolutional neural networks on graphs with fast localized spectral filtering
Defferrard, M., Bresson, X., and Vandergheynst, P · 2016
Cited alongside, same era.
Image style transfer using convolutional neural networks
Gatys, L. A., Ecker, A. S., and Bethge, M · 2016
Cited alongside, same era.
Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Cited alongside, same era.
Perceptual losses for real-time style transfer and super-resolution
Johnson, J., Alahi, A., and Fei-Fei, L · 2016
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Pointnet: Deep learning on point sets for 3d classification and segmentation
Qi, C. R., Su, H., Mo, K., and Guibas, L. J · 2017
Later among the works it cites.
Octnet: Learning deep 3d representations at high resolutions
Riegler, G., Ulusoy, A. O., and Geiger, A · 2017
Later among the works it cites.
Fully convolutional networks for semantic segmentation
Shelhamer, E., Long, J., and Darrell, T · 2017
Later among the works it cites.
Improved adversarial systems for 3d object generation and reconstruction
Smith, E. J. and Meger, D · 2017
Later among the works it cites.
Octree generating networks: Efficient convolutional architectures for high-resolution 3d outputs
Tatarchenko, M., Dosovitskiy, A., and Brox, T · 2017
Later among the works it cites.
Multi-view supervision for single-view reconstruction via differentiable ray consistency
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Kipf, T. N. and Welling, M · 2016
Cited alongside, same era.
Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling
Wu, J., Zhang, C., Xue, T., Freeman, W. T., and Tenenbaum, J. B · 2016
Cited alongside, same era.
Geometric deep learning: Going beyond euclidean data
Bronstein, M. M., Bruna, J., LeCun, Y., Szlam, A., and Vandergheynst, P · 2017
Cited alongside, same era.
A point set generation network for 3d object reconstruction from a single image
Fan, H., Su, H., and Guibas, L · 2017
Cited alongside, same era.
Inductive representation learning on large graphs
Hamilton, W., Ying, Z., and Leskovec, J · 2017
Cited alongside, same era.
Hierarchical surface prediction for 3d object reconstruction
Häne, C., Tulsiani, S., and Malik, J · 2017
Cited alongside, same era.
Mask r-cnn
He, K., Gkioxari, G., Dollár, P., and Girshick, R · 2017
Cited alongside, same era.
Tulsiani, S., Zhou, T., Efros, A. A., and Malik, J · 2017
Later among the works it cites.
MarrNet: 3D Shape Reconstruction via 2.5D Sketches
Wu, J., Wang, Y., Xue, T., Sun, X., Freeman, W. T., and Tenenbaum, J. B · 2017
Later among the works it cites.
On the iterative refinement of densely connected representation levels for semantic segmentation
Casanova, A., Cucurull, G., Drozdzal, M., Romero, A., and Bengio, Y · 2018
Later among the works it cites.
Convolutional neural networks for mesh-based parcellation of the cerebral cortex
Cucurull, G., Wagstyl, K., Casanova, A., Velickovic, P., Jakobsen, E., Drozdzal, M., Romero, A., Evans, A., and Bengio, Y · 2018
Later among the works it cites.
Learning to generate and reconstruct 3d meshes with only 2d supervision
Henderson, P. and Ferrari, V · 2018
Later among the works it cites.
Unsupervised learning of shape and pose with differentiable point clouds
Insafutdinov, E. and Dosovitskiy, A · 2018
Later among the works it cites.
Learning free-form deformations for 3d object reconstruction
Jack, D., Pontes, J. K., Sridharan, S., Fookes, C., Shirazi, S., Maire, F., and Eriksson, A · 2018
Later among the works it cites.
Learning category-specific mesh reconstruction from image collections
Kanazawa, A., Tulsiani, S., Efros, A. A., and Malik, J · 2018
Later among the works it cites.
Deeper insights into graph convolutional networks for semi-supervised learning
Li, Q., Han, Z., and Wu, X.-M · 2018
Later among the works it cites.
Multi-view silhouette and depth decomposition for high resolution 3d object representation
Smith, E., Fujimoto, S., and Meger, D · 2018
Later among the works it cites.
Pix3d: Dataset and methods for single-image 3d shape modeling
Sun, X., Wu, J., Zhang, X., Zhang, Z., Zhang, C., Xue, T., Tenenbaum, J. B., and Freeman, W. T · 2018
Later among the works it cites.
Graph Attention Networks
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y · 2018
Later among the works it cites.
Pixel2mesh: Generating 3d mesh models from single rgb images
Wang, N., Zhang, Y., Li, Z., Fu, Y., Liu, W., and Jiang, Y.-G · 2018
Later among the works it cites.
Learning shape priors for single-view 3d completion and reconstruction
Wu, J., Zhang, C., Zhang, X., Zhang, Z., Freeman, W. T., and Tenenbaum, J. B · 2018
Later among the works it cites.