Fetching the paper…
Reading the bibliography…
We present a StyleGAN2-based deep learning approach for 3D shape generation, called SDF-StyleGAN, with the aim of reducing visual and geometric dissimilarity between generated shapes and a shape collection.
Marching cubes: A high resolution 3D surface construction algorithm
Lorensen W. E., Cline H. E · 1987
Earlier work this paper cites.
On Visual similarity based 3D model retrieval
Chen D.-Y., Tian X.-P., Shen Y.-T., Ouhyoung M · 2003
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Deng J., Dong W., Socher R., Li L.-J., Li K., Fei-Fei L · 2009
Earlier work this paper cites.
Screened poisson surface reconstruction
Kazhdan M., Hoppe H · 2013
Earlier work this paper cites.
Generative adversarial nets
Goodfellow I., Pouget-Abadie J., Mirza M., Xu B., Warde-Farley D., Ozair S., Courville A., Bengio Y · 2014
Earlier work this paper cites.
Auto-encoding variational Bayes
Kingma D. P., Welling M · 2014
Earlier work this paper cites.
Signed distance fields for polygon soup meshes
Xu H., Barbič J · 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.
Variational inference with normalizing flows
Rezende D., Mohamed S · 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., Savarese S · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
He K., Zhang X., Ren S., Sun J · 2016
Earlier work this paper cites.
Rethinking the inception architecture for computer vision
Szegedy C., Vanhoucke V., Ioffe S., Shlens J., Wojna Z · 2016
Earlier work this paper cites.
Pixel recurrent neural networks
Van Oord A., Kalchbrenner N., Kavukcuoglu K · 2016
Earlier work this paper cites.
Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling
Wu J., Zhang C., Xue T., Freeman W. T., Tenenbaum J. B · 2016
Earlier work this paper cites.
A theory of generative convnet
Xie J., Lu Y., Zhu S.-C., Wu Y · 2016
Earlier work this paper cites.
A new graph-based two-sample test for multivariate and object data
Chen H., Friedman J. H · 2017
Earlier work this paper cites.
GANs trained by a two time-scale update rule converge to a local NASH equilibrium
Heusel M., Ramsauer H., Unterthiner T., Nessler B., Hochreiter S · 2017
Earlier work this paper cites.
Hierarchical detail enhancing mesh-based shape generation with 3D generative adversarial network
Jiang C., Marcus P., et al · 2017
Earlier work this paper cites.
GRASS: Generative recursive autoencoders for shape structures
Li J., Xu K., Chaudhuri S., Yumer E., Zhang H., Guibas L · 2017
Earlier work this paper cites.
PointNet: Deep learning on point sets for 3D classification and segmentation
Qi C. R., Su H., Mo K., Guibas L. J · 2017
Earlier work this paper cites.
Improved adversarial systems for 3D object generation and reconstruction
Smith E. J., Meger D · 2017
Earlier work this paper cites.
O-CNN: Octree-based convolutional neural networks for 3D shape analysis
Wang P.-S., Liu Y., Guo Y.-X., Sun C.-Y., Tong X · 2017
Earlier work this paper cites.
Learning representations and generative models for 3D point clouds
Achlioptas P., Diamanti O., Mitliagkas I., Guibas L · 2018
Earlier work this paper cites.
AtlasNet: A Papier-Mâché approach to learning 3D surface generation
Groueix T., Fisher M., Kim V. G., Russell B. C., Aubry M · 2018
Earlier work this paper cites.
Which training methods for GANs do actually converge?
Mescheder L., Geiger A., Nowozin S · 2018
Earlier work this paper cites.
Tags2Parts: Discovering semantic regions from shape tags
Muralikrishnan S., Kim V. G., Chaudhuri S · 2018
Earlier work this paper cites.
Global-to-local generative model for 3D shapes
Wang H., Schor N., Hu R., Huang H., Cohen-Or D., Huang H · 2018
Earlier work this paper cites.
Pixel2Mesh: Generating 3D mesh models from single rgb images
Wang N., Zhang Y., Li Z., Fu Y., Liu W., Jiang Y.-G · 2018
Earlier work this paper cites.
The unusual effectiveness of averaging in GAN training
Yaz Y., Foo C.-S., Winkler S., Yap K.-H., Piliouras G., Chandrasekhar V., et al · 2018
Cited alongside, same era.
Learning implicit fields for generative shape modeling
Chen Z., Zhang H · 2019
Cited alongside, same era.
Composite shape modeling via latent space factorization
Dubrovina A., Xia F., Achlioptas P., Shalah M., Groscot R., Guibas L. J · 2019
Cited alongside, same era.
SDM-NET: Deep generative network for structured deformable mesh
Gao L., Yang J., Wu T., Yuan Y.-J., Fu H., Lai Y.-K., Zhang H · 2019
Cited alongside, same era.
3D volumetric modeling with introspective neural networks
Huang W., Lai B., Xu W., Tu Z · 2019
Cited alongside, same era.
A style-based generator architecture for generative adversarial networks
C-flow: Conditional generative flow models for images and 3D point clouds
Pumarola A., Popov S., Moreno-Noguer F., Ferrari V · 2020
Later among the works it cites.
PointGrow: Autoregressively learned point cloud generation with self-attention
Sun Y., Wang Y., Liu Z., Siegel J. E., Sarma S. E · 2020
Later among the works it cites.
Interfacegan: Interpreting the disentangled face representation learned by GANs
Shen Y., Yang C., Tang X., Zhou B · 2020
Later among the works it cites.
DFR: Differentiable function rendering for learning 3D generation from images
Wu Y., Sun Z · 2020
Later among the works it cites.
PQ-NET: A generative part Seq2Seq network for 3D shapes
Wu R., Zhuang Y., Xu K., Zhang H., Chen B · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Karras T., Laine S., Aila T · 2019
Cited alongside, same era.
Synthesizing 3D shapes from silhouette image collections using multi-projection generative adversarial networks
Li X., Dong Y., Peers P., Tong X · 2019
Cited alongside, same era.
Decoupled Weight Decay Regularization
Loshchilov I., Hutter F · 2019
Cited alongside, same era.
StructureNet: Hierarchical graph networks for 3D shape generation
Mo K., Guerrero P., Yi L., Su H., Wonka P., Mitra N. J., Guibas L. J · 2019
Cited alongside, same era.
Occupancy Networks: Learning 3D reconstruction in function space
Mescheder L., Oechsle M., Niemeyer M., Nowozin S., Geiger A · 2019
Cited alongside, same era.
DeepSDF: Learning continuous signed distance functions for shape representation
Park J. J., Florence P., Straub J., Newcombe R., Lovegrove S · 2019
Cited alongside, same era.
Generative modeling by estimating gradients of the data distribution
Song Y., Ermon S · 2019
Cited alongside, same era.
Generative VoxelNet: Learning energy-based models for 3D shape synthesis and analysis
Xie J., Zheng Z., Gao R., Wang W., Zhu S.-C., Wu Y. N · 2020
Later among the works it cites.
DECOR-GAN: 3D shape detailization by conditional refinement
Chen Z., Kim V. G., Fisher M., Aigerman N., Zhang H., Chaudhuri S · 2021
Later among the works it cites.
pi-gan: Periodic implicit generative adversarial networks for 3D-aware image synthesis
Chan E. R., Monteiro M., Kellnhofer P., Wu J., Wetzstein G · 2021
Later among the works it cites.
Octree Transformer: Autoregressive 3D shape generation on hierarchically structured sequences
Ibing M., Kobsik G., Kobbelt L · 2021
Later among the works it cites.
3D shape generation with grid-based implicit functions
Ibing M., Lim I., Kobbelt L · 2021
Later among the works it cites.
Alias-Free generative adversarial networks
Karras T., Aittala M., Laine S., Härkönen E., Hellsten J., Lehtinen J., Aila T · 2021
Later among the works it cites.
ChartPointFlow for topology-aware 3D point cloud generation
Kimura T., Matsubara T., Uehara K · 2021
Later among the works it cites.
Deep implicit moving least-squares functions for 3D reconstruction
Liu S.-L., Guo H.-X., Pan H., Wang P.-S., Tong X., Liu Y · 2021
Later among the works it cites.
Diffusion probabilistic models for 3D point cloud generation
Luo S., Hu W · 2021
Later among the works it cites.
SP-GAN: Sphere-guided 3D shape generation and manipulation
Li R., Li X., Hui K.-H., Fu C.-W · 2021
Later among the works it cites.
SurfGen: Adversarial 3D shape synthesis with explicit surface discriminators
Luo A., Li T., Zhang W.-H., Lee T. S · 2021
Later among the works it cites.
Go with the flows: Mixtures of normalizing flows for point cloud generation and reconstruction
Postels J., Liu M., Spezialetti R., Van Gool L., Tombari F · 2021
Later among the works it cites.
On aliased resizing and surprising subtleties in GAN evaluation
Parmar G., Zhang R., Zhu J.-Y · 2021
Later among the works it cites.
Learning progressive point embeddings for 3D point cloud generation
Wen C., Yu B., Tao D · 2021
Later among the works it cites.
Generative PointNet: Deep energy-based learning on unordered point sets for 3D generation, reconstruction and classification
Xie J., Xu Y., Zheng Z., Zhu S.-C., Wu Y. N · 2021
Later among the works it cites.
Pointr: Diverse point cloud completion with geometry-aware transformers
Yu X., Rao Y., Wang Z., Liu Z., Lu J., Zhou J · 2021
Later among the works it cites.
3D shape generation and completion through point-voxel diffusion
Zhou L., Du Y., Wu J · 2021
Later among the works it cites.
EditVAE: Unsupervised part-aware controllable 3D point cloud shape generation
Li S., Liu M., Walder C · 2022
Closest in time.
AutoSDF: Shape priors for 3D completion, reconstruction and generation
Mittal P., Cheng Y.-C., Singh M., Tulsiani S · 2022
Closest in time.
StyleSDF: High-resolution 3D-Consistent image and geometry generation
Or-El R., Luo X., Shan M., Shechtman E., Park J. J., Kemelmacher-Shlizerman I · 2022
Closest in time.
Dual octree graph networks for learning adaptive volumetric shape representations
Wang P.-S., Liu Y., Tong X · 2022
Closest in time.
DSG-Net: Learning Disentangled Structure and Geometry for 3D Shape Generation
Yang J., Mo K., Lai Y.-K., Guibas L. J., Gao L · 2022
Closest in time.
MRGAN: Multi-rooted 3D shape representation learning with unsupervised part disentanglement
Gal R., Bermano A., Zhang H., Cohen-Or D · 2048
Closest in time.