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Diffusion models have emerged as a popular method for 3D generation.
“Marching Cubes: A High Resolution 3D Surface Construction Algorithm”
William. Lorensen and Harvey. Cline · 1987
Earlier work this paper cites.
“Multi-level partition of unity implicits”
Yutaka Ohtake et al · 2003
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“Auto-encoding variational Bayes”
Diederik. Kingma and Max Welling · 2013
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“ShapeNet: An information-rich 3D model repository”
Angel. Chang et al · 2015
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“U-Net: Convolutional networks for biomedical image segmentation”
Olaf Ronneberger, Philipp Fischer and Thomas Brox · 2015
Earlier work this paper cites.
“Deep Unsupervised Learning using Nonequilibrium Thermodynamics”
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan and Surya Ganguli · 2015
Earlier work this paper cites.
“3D ShapeNets: A deep representation for volumetric shape modeling”
Zhirong Wu et al · 2015
Earlier work this paper cites.
“Generative adversarial networks”
Ian Goodfellow et al · 2016
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“Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling”
Jiajun Wu et al · 2016
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“A point set generation network for 3D object reconstruction from a single image”
Haoqiang Fan, Hao Su and Leonidas. Guibas · 2017
Earlier work this paper cites.
“O-CNN: Octree-based convolutional neural networks for 3D shape analysis”
Peng-Shuai Wang et al · 2017
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“Learning representations and generative models for 3D point clouds”
Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas and Leonidas. Guibas · 2018
Earlier work this paper cites.
“Text2Shape: Generating Shapes from Natural Language by Learning Joint Embeddings”
Kevin Chen et al · 2018
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“Adaptive O-CNN: A patch-based deep representation of 3D shapes”
Peng-Shuai Wang, Chun-Yu Sun, Yang Liu and Xin Tong · 2018
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“4D spatio-temporal convnets: Minkowski convolutional neural networks”
Christopher Choy, JunYoung Gwak and Silvio Savarese · 2019
Earlier work this paper cites.
“Learning implicit fields for generative shape modeling”
Zhiqin Chen and Hao Zhang · 2019
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“Decoupled weight decay regularization”
Ilya Loshchilov and Frank Hutter · 2019
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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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“Occupancy networks: Learning 3D reconstruction in function space”
Lars Mescheder et al · 2019
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“DeepSDF: Learning continuous signed distance functions for shape representation”
Jeong Park et al · 2019
Earlier work this paper cites.
“Pointflow: 3D point cloud generation with continuous normalizing flows”
Guandao Yang et al · 2019
Earlier work this paper cites.
“Denoising diffusion probabilistic models”
Jonathan Ho, Ajay Jain and Pieter Abbeel · 2020
Earlier work this paper cites.
“NeRF: Representing scenes as neural radiance fields for view synthesis”
Ben Mildenhall et al · 2020
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“Convolutional occupancy networks”
Songyou Peng et al · 2020
Earlier work this paper cites.
“Diffusion Models Beat GANs on Image Synthesis”
Prafulla Dhariwal and Alexander Nichol · 2021
Earlier work this paper cites.
“Variational diffusion models”
Diederik Kingma, Tim Salimans, Ben Poole and Jonathan Ho · 2021
Cited alongside, same era.
“Diffusion Probabilistic Models for 3D Point Cloud Generation”
Shitong Luo and Wei Hu · 2021
Cited alongside, same era.
“Improved Denoising Diffusion Probabilistic Models”
Alexander Nichol and Prafulla Dhariwal · 2021
Cited alongside, same era.
“3D Shape Generation and Completion Through Point-Voxel Diffusion”
Linqi Zhou, Yilun Du and Jiajun Wu · 2021
Cited alongside, same era.
“GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images”
Jun Gao et al · 2022
Cited alongside, same era.
“Neural Wavelet-Domain Diffusion for 3D Shape Generation”
Ka-Hei Hui, Ruihui Li, Jingyu Hu and Chi-Wing Fu · 2022
Cited alongside, same era.
“Nerdi: Single-view nerf synthesis with language-guided diffusion as general image priors”
Congyue Deng et al · 2023
Later among the works it cites.
“Objaverse: A universe of annotated 3D objects”
Matt Deitke et al · 2023
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“HyperDiffusion: Generating Implicit Neural Fields with Weight-Space Diffusion”
Ziya Erkoç et al · 2023
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“3DGen: Triplane latent diffusion for textured mesh generation”
Anchit Gupta et al · 2023
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Jiatao Gu et al · 2023
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“Simple Diffusion: End-to-End Diffusion for High Resolution Images”
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“SPAGHETTI: Editing Implicit Shapes Through Part Aware Generation”
Amir Hertz et al · 2022
Cited alongside, same era.
“Cascaded diffusion models for high fidelity image generation”
Jonathan Ho et al · 2022
Cited alongside, same era.
“Scalable adaptive computation for iterative generation”
Allan Jabri, David Fleet and Ting Chen · 2022
Cited alongside, same era.
“AutoSDF: Shape Priors for 3D Completion, Reconstruction and Generation”
Paritosh Mittal, Yen-Chi Cheng, Maneesh Singh and Shubham Tulsiani · 2022
Cited alongside, same era.
“Instant Neural Graphics Primitives with a Multiresolution Hash Encoding”
Thomas Müller, Alex Evans, Christoph Schied and Alexander Keller · 2022
Cited alongside, same era.
“Point-E: A system for generating 3D point clouds from complex prompts”
Alex Nichol et al · 2022
Cited alongside, same era.
Emiel Hoogeboom, Jonathan Heek and Tim Salimans · 2023
Later among the works it cites.
“Shap-E: Generating conditional 3D implicit functions”
Heewoo Jun and Alex Nichol · 2023
Later among the works it cites.
“Diffusion-SDF: Text-to-Shape via Voxelized Diffusion”
Muheng Li, Yueqi Duan, Jie Zhou and Jiwen Lu · 2023
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“MeshDiffusion: Score-based Generative 3D Mesh Modeling”
Zhen Liu et al · 2023
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“Magic3D: High-resolution text-to-3D content creation”
Chen-Hsuan Lin et al · 2023
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Minghua Liu et al · 2023
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“DreamFusion: Text-to-3D using 2D Diffusion”
Ben Poole, Ajay Jain, Jonathan. Barron and Ben Mildenhall · 2023
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“Magic123: One image to high-quality 3D object generation using both 2D and 3D diffusion priors”
Guocheng Qian et al · 2023
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“3D Neural Field Generation using Triplane Diffusion”
J. Shue et al · 2023
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“Diffusion-based Signed Distance Fields for 3D Shape Generation”
Jaehyeok Shim, Changwoo Kang and Kyungdon Joo · 2023
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“Mvdream: Multi-view diffusion for 3D generation”
Yichun Shi et al · 2023
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“Make-it-3d: High-fidelity 3d creation from a single image with diffusion prior”
Junshu Tang et al · 2023
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Zhengyi Wang et al · 2023
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“Locally Attentional SDF Diffusion for Controllable 3D Shape Generation”
Xin-Yang Zheng et al · 2023
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“3DShape2VecSet: A 3D shape representation for neural fields and generative diffusion models”
Biao Zhang, Jiapeng Tang, Matthias Niessner and Peter Wonka · 2023
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“Large-Vocabulary 3D Diffusion Model with Transformer”
Ziang Cao et al · 2024
Closest in time.
“XCube ( 𝒳 3 \mathcal{X}^{3} ): Large-Scale 3D Generative Modeling using Sparse Voxel Hierarchies”
Xuanchi Ren et al · 2024
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“LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation”
Jiaxiang Tang et al · 2024
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“Structured 3D Latents for Scalable and Versatile 3D Generation”
Jianfeng Xiang et al · 2024
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