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2D-to-3D reconstruction is an ill-posed problem, yet humans are good at solving this problem due to their prior knowledge of the 3D world developed over years.
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Shapenet: An information-rich 3d model repository
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Denoising diffusion implicit models
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Multiview neural surface reconstruction by disentangling geometry and appearance
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pi-gan: Periodic implicit generative adversarial networks for 3d-aware image synthesis
Eric R Chan, Marco Monteiro, Petr Kellnhofer, Jiajun Wu, and Gordon Wetzstein · 2021
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Mvsnerf: Fast generalizable radiance field reconstruction from multi-view stereo
Anpei Chen, Zexiang Xu, Fuqiang Zhao, Xiaoshuai Zhang, Fanbo Xiang, Jingyi Yu, and Hao Su · 2021
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Stereo radiance fields (srf): Learning view synthesis for sparse views of novel scenes
Julian Chibane, Aayush Bansal, Verica Lazova, and Gerard Pons-Moll · 2021
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Putting nerf on a diet: Semantically consistent few-shot view synthesis
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Diffusion probabilistic models for 3d point cloud generation
Shitong Luo and Wei Hu · 2021
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Sdedit: Image synthesis and editing with stochastic differential equations
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Efficient geometry-aware 3d generative adversarial networks
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Google scanned objects: A high-quality dataset of 3d scanned household items
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An image is worth one word: Personalizing text-to-image generation using textual inversion
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Gnerf: Gan-based neural radiance field without posed camera
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Nerf: Representing scenes as neural radiance fields for view synthesis
Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng · 2021
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Learning transferable visual models from natural language supervision
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Pre-train, self-train, distill: A simple recipe for supersizing 3d reconstruction
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Novel view synthesis with diffusion models
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