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Diffusion models have emerged as a powerful tool for point cloud generation.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Generative and discriminative voxel modeling with convolutional neural networks
Andrew Brock, Theodore Lim, James M Ritchie, and Nick Weston · 2016
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Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling
Jiajun Wu, Chengkai Zhang, Tianfan Xue, William T Freeman, and Joshua B Tenenbaum · 2016
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Learning representations and generative models for 3d point clouds
Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, and Leonidas Guibas · 2018
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Neural ordinary differential equations
Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud · 2018
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Learning to reconstruct shapes from unseen classes
Xiuming Zhang, Zhoutong Zhang, Chengkai Zhang, Josh Tenenbaum, Bill Freeman, and Jiajun Wu · 2018
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Point-voxel cnn for efficient 3d deep learning
Zhijian Liu, Haotian Tang, Yujun Lin, and Song Han · 2019
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3d point cloud generative adversarial network based on tree structured graph convolutions
Dong Wook Shu, Sung Woo Park, and Junseok Kwon · 2019
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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Theoretical guarantees for sampling and inference in generative models with latent diffusions
Belinda Tzen and Maxim Raginsky · 2019
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Pointflow: 3d point cloud generation with continuous normalizing flows
Guandao Yang, Xun Huang, Zekun Hao, Ming-Yu Liu, Serge Belongie, and Bharath Hariharan · 2019
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nuscenes: A multimodal dataset for autonomous driving
Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom · 2020
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Learning gradient fields for shape generation
Ruojin Cai, Guandao Yang, Hadar Averbuch-Elor, Zekun Hao, Serge Belongie, Noah Snavely, and Bharath Hariharan · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
Softflow: Probabilistic framework for normalizing flow on manifolds
Hyeongju Kim, Hyeonseung Lee, Woo Hyun Kang, Joun Yeop Lee, and Nam Soo Kim · 2020
Cited alongside, same era.
Discrete point flow networks for efficient point cloud generation
Roman Klokov, Edmond Boyer, and Jakob Verbeek · 2020
Cited alongside, same era.
Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2020
Cited alongside, same era.
Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2020
Cited alongside, same era.
Diffusion models beat gans on image synthesis
Multimodal virtual point 3d detection
Tianwei Yin, Xingyi Zhou, and Philipp Krähenbühl · 2021
Later among the works it cites.
3d shape generation and completion through point-voxel diffusion
Linqi Zhou, Yilun Du, and Jiajun Wu · 2021
Later among the works it cites.
Building normalizing flows with stochastic interpolants
Michael S Albergo and Eric Vanden-Eijnden · 2022
Closest in time.
Get3d: A generative model of high quality 3d textured shapes learned from images
Jun Gao, Tianchang Shen, Zian Wang, Wenzheng Chen, Kangxue Yin, Daiqing Li, Or Litany, Zan Gojcic, and Sanja Fidler · 2022
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Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and David J Fleet · 2022
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Prafulla Dhariwal and Alexander Nichol · 2021
Cited alongside, same era.
Setvae: Learning hierarchical composition for generative modeling of set-structured data
Jinwoo Kim, Jaehoon Yoo, Juho Lee, and Seunghoon Hong · 2021
Cited alongside, same era.
On fast sampling of diffusion probabilistic models
Zhifeng Kong and Wei Ping · 2021
Cited alongside, same era.
Sp-gan: Sphere-guided 3d shape generation and manipulation
Ruihui Li, Xianzhi Li, Ka-Hei Hui, and Chi-Wing Fu · 2021
Cited alongside, same era.
Fusedream: Training-free text-to-image generation with improved clip+ gan space optimization
Xingchao Liu, Chengyue Gong, Lemeng Wu, Shujian Zhang, Hao Su, and Qiang Liu · 2021
Cited alongside, same era.
Knowledge distillation in iterative generative models for improved sampling speed
Eric Luhman and Troy Luhman · 2021
Cited alongside, same era.
Diffusion probabilistic models for 3d point cloud generation
Shitong Luo and Wei Hu · 2021
Cited alongside, same era.
Yaron Lipman, Ricky TQ Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le · 2022
Closest in time.
Rectified flow: A marginal preserving approach to optimal transport
Qiang Liu · 2022
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Flow straight and fast: Learning to generate and transfer data with rectified flow
Xingchao Liu, Chengyue Gong, and Qiang Liu · 2022
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Text2mesh: Text-driven neural stylization for meshes
Oscar Michel, Roi Bar-On, Richard Liu, Sagie Benaim, and Rana Hanocka · 2022
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Hierarchical text-conditional image generation with clip latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Progressive distillation for fast sampling of diffusion models
Tim Salimans and Jonathan Ho · 2022
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Diffusion probabilistic modeling for video generation
Ruihan Yang, Prakhar Srivastava, and Stephan Mandt · 2022
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Lion: Latent point diffusion models for 3d shape generation
Xiaohui Zeng, Arash Vahdat, Francis Williams, Zan Gojcic, Or Litany, Sanja Fidler, and Karsten Kreis · 2022
Closest in time.
Neural volumetric mesh generator
Yan Zheng, Lemeng Wu, Xingchao Liu, Zhen Chen, Qiang Liu, and Qixing Huang · 2022
Closest in time.