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This paper presents a new approach for 3D shape generation, inversion, and manipulation, through a direct generative modeling on a continuous implicit representation in wavelet domain.
Marching Cubes: A high resolution 3D surface construction algorithm. In Proceedings of SIGGRAPH , Vol. 21. 163–169
William E. Lorensen and Harvey E. Cline. 1987 · 1987
Earlier work this paper cites.
A theory for multiresolution signal decomposition: the wavelet representation
Stephane G. Mallat. 1989 · 1989
Earlier work this paper cites.
The wavelet transform, time-frequency localization and signal analysis
Ingrid Daubechies. 1990 · 1990
Earlier work this paper cites.
Biorthogonal wavelets
Albert Cohen. 1992 · 1992
Earlier work this paper cites.
Multiscale implicit models. In Proceedings of SIBGRAPI , Vol. 94. 93–100
Luiz Velho, Demetri Terzopoulos, and Jonas Gomes. 1994 · 1994
Earlier work this paper cites.
On visual similarity based 3D model retrieval. In Computer Graphics Forum , Vol. 22. 223–232
Ding-Yun Chen, Xiao-Pei Tian, Yu-Te Shen, and Ming Ouhyoung. 2003 · 2003
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
ShapeNet: An information-rich 3D model repository
Angel X. Chang, Thomas Funkhouser, Leonidas J. Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al · 2015
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics. In Proceedings of International Conference on Machine Learning (ICML) . 2256–2265
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. 2015 · 2015
Earlier work this paper cites.
Learning a predictable and generative vector representation for objects. In European Conference on Computer Vision (ECCV) . 484–499
Rohit Girdhar, David F. Fouhey, Mikel Rodriguez, and Abhinav Gupta. 2016 · 2016
Earlier work this paper cites.
Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling. In Conference on Neural Information Processing Systems (NeurIPS) . 82–90
Jiajun Wu, Chengkai Zhang, Tianfan Xue, Bill Freeman, and Josh Tenenbaum. 2016 · 2016
Earlier work this paper cites.
Generative visual manipulation on the natural image manifold. In European Conference on Computer Vision (ECCV) . 597–613
Jun-Yan Zhu, Philipp Krähenbühl, Eli Shechtman, and Alexei A. Efros. 2016 · 2016
Earlier work this paper cites.
A point set generation network for 3D object reconstruction from a single image. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 605–613
Haoqiang Fan, Hao Su, and Leonidas J. Guibas. 2017 · 2017
Earlier work this paper cites.
Bendsketch: Modeling freeform surfaces through 2D sketching
Changjian Li, Hao Pan, Yang Liu, Xin Tong, Alla Sheffer, and Wenping Wang. 2017a · 2017
Earlier work this paper cites.
GRASS: Generative recursive autoencoders for shape structures
Jun Li, Kai Xu, Siddhartha Chaudhuri, Ersin Yumer, Hao Zhang, and Leonidas J. Guibas. 2017b · 2017
Earlier work this paper cites.
Improved adversarial systems for 3D object generation and reconstruction. In Conference on Robot Learning . PMLR, 87–96
Edward J. Smith and David Meger. 2017 · 2017
Earlier work this paper cites.
Rui Zhu, Hamed Kiani Galoogahi, Chaoyang Wang, and Simon Lucey. 2017 · 2017
Earlier work this paper cites.
Learning representations and generative models for 3D point clouds. In Proceedings of International Conference on Machine Learning (ICML) . 40–49
Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, and Leonidas J. Guibas. 2018 · 2018
Earlier work this paper cites.
A papier-mâché approach to learning 3D surface generation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 216–224
Thibault Groueix, Matthew Fisher, Vladimir G. Kim, Bryan C. Russell, and Mathieu Aubry. 2018 · 2018
Earlier work this paper cites.
GAL: Geometric adversarial loss for single-view 3D-object reconstruction. In European Conference on Computer Vision (ECCV) . 802–816
Li Jiang, Shaoshuai Shi, Xiaojuan Qi, and Jiaya Jia. 2018 · 2018
Earlier work this paper cites.
Pixel2Mesh: Generating 3D mesh models from single RGB images. In European Conference on Computer Vision (ECCV) . 52–67
Nanyang Wang, Yinda Zhang, Zhuwen Li, Yanwei Fu, Wei Liu, and Yu-Gang Jiang. 2018 · 2018
Earlier work this paper cites.
Learning single-view 3D reconstruction with limited pose supervision. In European Conference on Computer Vision (ECCV) . 86–101
Guandao Yang, Yin Cui, Serge Belongie, and Bharath Hariharan. 2018 · 2018
Earlier work this paper cites.
Semantic Photo Manipulation with a Generative Image Prior
David Bau, Hendrik Strobelt, William Peebles, Jonas Wulff, Bolei Zhou, Jun-Yan Zhu, and Antonio Torralba. 2019 · 2019
Earlier work this paper cites.
Learning implicit fields for generative shape modeling. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 5939–5948
Zhiqin Chen and Hao Zhang. 2019 · 2019
Earlier work this paper cites.
Learning to infer implicit surfaces without 3D supervision
Shichen Liu, Shunsuke Saito, Weikai Chen, and Hao Li. 2019 · 2019
Earlier work this paper cites.
Occupancy networks: Learning 3D reconstruction in function space. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 4460–4470
Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. 2019 · 2019
Earlier work this paper cites.
StructureNet: Hierarchical graph networks for 3D shape generation
Kaichun Mo, Paul Guerrero, Li Yi, Hao Su, Peter Wonka, Niloy J. Mitra, and Leonidas J. Guibas. 2019 · 2019
Earlier work this paper cites.
DeepSDF: Learning continuous signed distance functions for shape representation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 165–174
Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove. 2019 · 2019
Earlier work this paper cites.
GEOMetrics: Exploiting geometric structure for graph-encoded objects. In Proceedings of International Conference on Machine Learning (ICML) . 5866–5876
Edward J. Smith, Scott Fujimoto, Adriana Romero, and David Meger. 2019 · 2019
Cited alongside, same era.
A skeleton-bridged deep learning approach for generating meshes of complex topologies from single RGB images. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 4541–4550
Jiapeng Tang, Xiaoguang Han, Junyi Pan, Kui Jia, and Xin Tong. 2019 · 2019
Cited alongside, same era.
DISN: Deep implicit surface network for high-quality single-view 3D reconstruction. In Conference on Neural Information Processing Systems (NeurIPS) . 490–500
Qiangeng Xu, Weiyue Wang, Duygu Ceylan, Radomir Mech, and Ulrich Neumann. 2019 · 2019
Cited alongside, same era.
PointFlow: 3D point cloud generation with continuous normalizing flows. In IEEE International Conference on Computer Vision (ICCV) . 4541–4550
Guandao Yang, Xun Huang, Zekun Hao, Ming-Yu Liu, Serge Belongie, and Bharath Hariharan. 2019 · 2019
Cited alongside, same era.
3D Shape Generation With Grid-Based Implicit Functions. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 13559–13568
Moritz Ibing, Isaak Lim, and Leif Kobbelt. 2021 · 2021
Later among the works it cites.
D 2 IM-Net: Learning detail disentangled implicit fields from single images. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 10246–10255
Manyi Li and Hao Zhang. 2021 · 2021
Later among the works it cites.
SP-GAN: Sphere-Guided 3D Shape Generation and Manipulation
Ruihui Li, Xianzhi Li, Ke-Hei Hui, and Chi-Wing Fu. 2021 · 2021
Later among the works it cites.
Deep Implicit Moving Least-Squares Functions for 3D Reconstruction. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 1788–1797
Shi-Lin Liu, Hao-Xiang Guo, Hao Pan, Pengshuai Wang, Xin Tong, and Yang Liu. 2021 · 2021
Later among the works it cites.
SurfGen: Adversarial 3D Shape Synthesis with Explicit Surface Discriminators. In IEEE International Conference on Computer Vision (ICCV) . 16238–16248
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SAL: Sign agnostic learning of shapes from raw data. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 2565–2574
Matan Atzmon and Yaron Lipman. 2020 · 2020
Cited alongside, same era.
Learning gradient fields for shape generation. In European Conference on Computer Vision (ECCV) . 364–381
Ruojin Cai, Guandao Yang, Hadar Averbuch-Elor, Zekun Hao, Serge Belongie, Noah Snavely, and Bharath Hariharan. 2020 · 2020
Cited alongside, same era.
Implicit functions in feature space for 3D shape reconstruction and completion. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 6970–6981
Julian Chibane, Thiemo Alldieck, and Gerard Pons-Moll. 2020 · 2020
Cited alongside, same era.
Uses of Complex Wavelets in Deep Convolutional Neural Networks
Fergal Cotter. 2020 · 2020
Cited alongside, same era.
Multiplicative filter networks. In International Conference on Learning Representations (ICLR)
Rizal Fathony, Anit Kumar Sahu, Devin Willmott, and J. Zico Kolter. 2020 · 2020
Cited alongside, same era.
Local deep implicit functions for 3D shape. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 4857–4866
Kyle Genova, Forrester Cole, Avneesh Sud, Aaron Sarna, and Thomas Funkhouser. 2020 · 2020
Cited alongside, same era.
Implicit geometric regularization for learning shapes. In Proceedings of International Conference on Machine Learning (ICML) . 3569–3579
Amos Gropp, Lior Yariv, Niv Haim, Matan Atzmon, and Yaron Lipman. 2020 · 2020
Cited alongside, same era.
DualSDF: Semantic shape manipulation using a two-level representation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 7631–7641
Zekun Hao, Hadar Averbuch-Elor, Noah Snavely, and Serge Belongie. 2020 · 2020
Cited alongside, same era.
Andrew Luo, Tianqin Li, Wen-Hao Zhang, and Tai Sing Lee. 2021 · 2021
Later among the works it cites.
Diffusion probabilistic models for 3D point cloud generation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 2837–2845
Shitong Luo and Wei Hu. 2021 · 2021
Later among the works it cites.
ACORN: Adaptive coordinate networks for neural scene representation
Julien N. P. Martel, David B. Lindell, Connor Z. Lin, Eric R. Chan, Marco Monteiro, and Gordon Wetzstein. 2021 · 2021
Later among the works it cites.
Improved denoising diffusion probabilistic models. In Proceedings of International Conference on Machine Learning (ICML) . 8162–8171
Alexander Quinn Nichol and Prafulla Dhariwal. 2021 · 2021
Later among the works it cites.
Neural geometric level of detail: Real-time rendering with implicit 3D shapes. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 11358–11367
Towaki Takikawa, Joey Litalien, Kangxue Yin, Karsten Kreis, Charles Loop, Derek Nowrouzezahrai, Alec Jacobson, Morgan McGuire, and Sanja Fidler. 2021 · 2021
Later among the works it cites.
SkeletonNet: A topology-preserving solution for learning mesh reconstruction of object surfaces from RGB images
Jiapeng Tang, Xiaoguang Han, Mingkui Tan, Xin Tong, and Kui Jia. 2021 · 2021
Later among the works it cites.
Designing an encoder for StyleGAN image manipulation
Omer Tov, Yuval Alaluf, Yotam Nitzan, Or Patashnik, and Daniel Cohen-Or. 2021 · 2021
Later among the works it cites.
Unsupervised 3D shape completion through gan inversion. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 1768–1777
Junzhe Zhang, Xinyi Chen, Zhongang Cai, Liang Pan, Haiyu Zhao, Shuai Yi, Chai Kiat Yeo, Bo Dai, and Chen Change Loy. 2021 · 2021
Later among the works it cites.
Sign-Agnostic Implicit Learning of Surface Self-Similarities for Shape Modeling and Reconstruction from Raw Point Clouds. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 10256–10265
Wenbin Zhao, Jiabao Lei, Yuxin Wen, Jianguo Zhang, and Kui Jia. 2021 · 2021
Later among the works it cites.
3D shape generation and completion through point-voxel diffusion. In IEEE International Conference on Computer Vision (ICCV) . 5826–5835
Linqi Zhou, Yilun Du, and Jiajun Wu. 2021 · 2021
Later among the works it cites.
GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images. In Conference on Neural Information Processing Systems (NeurIPS)
Jun Gao, Tianchang Shen, Zian Wang, Wenzheng Chen, Kangxue Yin, Daiqing Li, Or Litany, Zan Gojcic, and Sanja Fidler. 2022 · 2022
Later among the works it cites.
SPAGHETTI: Editing Implicit Shapes Through Part Aware Generation
Amir Hertz, Or Perel, Raja Giryes, Olga Sorkine-Hornung, and Daniel Cohen-Or. 2022 · 2022
Later among the works it cites.
Neural wavelet-domain diffusion for 3D shape generation. In Proceedings of SIGGRAPH ASIA . 9
Ka-Hei Hui, Ruihui Li, Jingyu Hu, and Chi-Wing Fu. 2022 · 2022
Later among the works it cites.
Neural Template: Topology-aware Reconstruction and Disentangled Generation of 3D Meshes. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Ka-Hei Hui*, Ruihui Li*, Jingyu Hu, and Chi-Wing Fu (* joint first authors). 2022 · 2022
Later among the works it cites.
NeuForm: Adaptive Overfitting for Neural Shape Editing. In Conference on Neural Information Processing Systems (NeurIPS)
Connor Z. Lin, Niloy J. Mitra, Gordon Wetzstein, Leonidas Guibas, and Paul Guerrero. 2022 · 2022
Later among the works it cites.
Towards Implicit Text-Guided 3D Shape Generation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 17896–17906
Zhengzhe Liu, Yi Wang, Xiaojuan Qi, and Chi-Wing Fu. 2022 · 2022
Later among the works it cites.
Repaint: Inpainting using denoising diffusion probabilistic models. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 11461–11471
Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, and Luc Van Gool. 2022 · 2022
Later among the works it cites.
Diffusion autoencoders: Toward a meaningful and decodable representation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 10619–10629
Konpat Preechakul, Nattanat Chatthee, Suttisak Wizadwongsa, and Supasorn Suwajanakorn. 2022 · 2022
Later among the works it cites.
MINER: Multiscale Implicit Neural Representations. In European Conference on Computer Vision (ECCV)
Vishwanath Saragadam, Jasper Tan, Guha Balakrishnan, Richard G. Baraniuk, and Ashok Veeraraghavan. 2022 · 2022
Later among the works it cites.
Gan inversion: A survey
Weihao Xia, Yulun Zhang, Yujiu Yang, Jing-Hao Xue, Bolei Zhou, and Ming-Hsuan Yang. 2022 · 2022
Later among the works it cites.
ShapeFormer: Transformer-based shape completion via sparse representation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 6239–6249
Xingguang Yan, Liqiang Lin, Niloy J. Mitra, Dani Lischinski, Daniel Cohen-Or, and Hui Huang. 2022 · 2022
Later among the works it cites.
SDF-StyleGAN: Implicit SDF-Based StyleGAN for 3D Shape Generation. In Eurographics Symposium on Geometry Processing (SGP)
Xin-Yang Zheng, Yang Liu, Peng-Shuai Wang, and Xin Tong. 2022 · 2022
Later among the works it cites.
MRGAN: Multi-Rooted 3D Shape Generation with Unsupervised Part Disentanglement. In IEEE International Conference on Computer Vision (ICCV) . 2039–2048
Rinon Gal, Amit Bermano, Hao Zhang, and Daniel Cohen-Or. 2020 · 2048
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