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In this work, we propose a novel method for generating 3D point clouds that leverage properties of hyper networks.
On information and sufficiency
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The earth mover’s distance as a metric for image retrieval
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3d convolutional neural networks for human action recognition
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
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3d contour closing: A local operator based on chamfer distance transformation
Tran, M.-P · 2013
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A survey of research on cloud robotics and automation
Kehoe, B., Patil, S., Abbeel, P., and Goldberg, K · 2015
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Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., and Frey, B · 2015
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Voxnet: A 3d convolutional neural network for real-time object recognition
Maturana, D. and Scherer, S · 2015
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Multi-view convolutional neural networks for 3d shape recognition
Su, H., Maji, S., Kalogerakis, E., and Learned-Miller, E · 2015
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3d shapenets: A deep representation for volumetric shapes
Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., and Xiao, J · 2015
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Ha, D., Dai, A., and Le, Q. V · 2016
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Conditional image generation with pixelcnn decoders
Van den Oord, A., Kalchbrenner, N., Espeholt, L., Vinyals, O., Graves, A., et al · 2016
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Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling
Wu, J., Zhang, C., Xue, T., Freeman, B., and Tenenbaum, J · 2016
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Learning representations and generative models for 3d point clouds
Achlioptas, P., Diamanti, O., Mitliagkas, I., and Guibas, L · 2017
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Stochastic maximum likelihood optimization via hypernetworks
Sheikh, A.-S., Rasul, K., Merentitis, A., and Bergmann, U · 2017
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Ffjord: Free-form continuous dynamics for scalable reversible generative models
Grathwohl, W., Chen, R. T., Betterncourt, J., Sutskever, I., and Duvenaud, D · 2018
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Li, C.-L., Zaheer, M., Zhang, Y., Poczos, B., and Salakhutdinov, R · 2018
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Pointgrow: Autoregressively learned point cloud generation with self-attention
Sun, Y., Wang, Y., Liu, Z., Siegel, J. E., and Sarma, S. E · 2018
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Pu-net: Point cloud upsampling network
Yu, L., Li, X., Fu, C.-W., Cohen-Or, D., and Heng, P.-A · 2018
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Adversarial autoencoders for compact representations of 3d point clouds
Zamorski, M., Zięba, M., Klukowski, P., Nowak, R., Kurach, K., Stokowiec, W., and Trzciński, T · 2018
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Tomczak, J. M. and Welling, M · 2017
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Deep sets
Zaheer, M., Kottur, S., Ravanbakhsh, S., Poczos, B., Salakhutdinov, R. R., and Smola, A. J · 2017
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Sylvester normalizing flows for variational inference
Berg, R. v. d., Hasenclever, L., Tomczak, J. M., and Welling, M · 2018
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Neural ordinary differential equations
Chen, T. Q., Rubanova, Y., Bettencourt, J., and Duvenaud, D. K · 2018
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Multiresolution tree networks for 3d point cloud processing
Gadelha, M., Wang, R., and Maji, S · 2018
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Pointnet: Deep learning on point sets for 3d classification and segmentation
Qi, C. R., Su, H., Mo, K., and Guibas, L. J
Cited in the paper.
Pointnet++: Deep hierarchical feature learning on point sets in a metric space
Qi, C. R., Yi, L., Su, H., and Guibas, L. J
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Hypernetwork functional image representation
Klocek, S., Maziarka, Ł., Wołczyk, M., Tabor, J., Nowak, J., and Śmieja, M · 2019
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Pointwise: An unsupervised point-wise feature learning network
Shoef, M., Fogel, S., and Cohen-Or, D · 2019
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Conditional invertible flow for point cloud generation
Stypułkowski, M., Zamorski, M., Zięba, M., and Chorowski, J · 2019
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Pointflow: 3d point cloud generation with continuous normalizing flows
Yang, G., Huang, X., Hao, Z., Liu, M.-Y., Belongie, S., and Hariharan, B · 2019
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Patch-based progressive 3d point set upsampling
Yifan, W., Wu, S., Huang, H., Cohen-Or, D., and Sorkine-Hornung, O · 2019
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