Fetching the paper…
Reading the bibliography…
Most algorithms that rely on deep learning-based approaches to generate 3D point sets can only produce clouds containing fixed number of points.
Random number generation and quasi-Monte Carlo methods , volume 63
Niederreiter, H · 1992
Earlier work this paper cites.
Surface Modeling with Oriented Particle Systems
Szeliski, R. and Tonnesen, D · 1992
Earlier work this paper cites.
From Multiple Stereo Views to Multiple 3D Surfaces
Fua, P · 1997
Earlier work this paper cites.
Empirical Evaluation of Dissimilarity Measures for Color and Texture
Rubner, Y., Puzicha, J., Tomasi, C., and Buhmann, J · 2001
Earlier work this paper cites.
On Visual Similarity Based 3D Model Retrieval, 2003
Chen, D.-Y., Tian, X.-P., Shen, Y., and Ouhyoung, M · 2003
Earlier work this paper cites.
Rotation Invariant Spherical Harmonic Representation of 3D Shape Descriptors
Kazhdan, M., Funkhouser, T., and Rusinkiewicz, S · 2003
Earlier work this paper cites.
Visualizing High Dimensional Data Using t-SNE
Maaten, L. and Hinton, G · 2008
Earlier work this paper cites.
Monocular 3D Reconstruction of Locally Textured Surfaces
Varol, A., Shaji, A., Salzmann, M., and Fua, P · 2012
Earlier work this paper cites.
The visionair infrastructure capabilities to support research
Attene, M., Giannini, F., Pitikakis, M., and Spagnuolo, M · 2013
Cited alongside, same era.
3d shapenets: A deep representation for volumetric shapes
Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., and Xiao, J · 2015
Cited alongside, same era.
Learning a predictable and generative vector representation for objects
Girdhar, R., Fouhey, D. F., Rodriguez, M., and Gupta, A · 2016
Cited alongside, same era.
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., and Sun, J · 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, B., and Tenenbaum, J · 2016
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
Learning Representations and Generative Models for 3D Point Clouds
Achlioptas, P., Diamanti, O., Mitliagkas, I., and Guibas, L · 2018
Later among the works it cites.
Point cloud gan
Li, C.-L., Zaheer, M., Zhang, Y., Poczos, B., and Salakhutdinov, R · 2018
Later among the works it cites.
Automated Top View Registration of Broadcast Football Videos
Sharma, R. A., Bhat, B., Gandhi, V., and Jawahar, C. V · 2018
Later among the works it cites.
Dynamic Graph CNN for Learning on Point Clouds
Wang, Y., Sun, Y., Liu, Z., Sarma, S., Bronstein, M., and Solomon, J · 2018
Later among the works it cites.
FoldingNet: Point Cloud Auto-Encoder via Deep Grid Deformation
Yang, Y., Feng, C., Shen, Y., and Tian, D · 2018
Later among the works it cites.
Pu-Net: Point Cloud Upsampling Network
Yu, L., Li, X., Fu, C., Cohen-Or, D., and Heng, P · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
The shape variational autoencoder: A deep generative model of part-segmented 3d objects
Nash, C. and Williams, C. K · 2017
Cited alongside, same era.
Progressive Minimal Path Method for Segmentation of 2D and 3D Line Structures
Liao, W., Worz, S., Kang, C., Cho, Z., and Rohr, K
Cited in the paper.
Deep Marching Cubes: Learning Explicit Surface Representations
Liao, Y., Donné, S., and Geiger, A
Cited in the paper.
Pointnet: Deep Learning on Point Sets for 3D Classification and Segmentation
Qi, C., Su, H., Mo, K., and Guibas, L
Cited in the paper.
Pointnet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
Qi, C., Yi, L., Su, H., and Guibas, L
Cited in the paper.
Adversarial autoencoders for generating 3d point clouds
Zamorski, M., Zieba, M., Nowak, R., Stokowiec, W., and Trzcinski, T · 2018
Later among the works it cites.