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Novel-view synthesis techniques achieve impressive results for static scenes but struggle when faced with the inconsistencies inherent to casual capture settings: varying illumination, scene motion, and other unintended effects that are difficult to model explicitly.
Photo tourism: exploring photo collections in 3D
Noah Snavely, Steven M Seitz, and Richard Szeliski · 2006
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Structure-from-motion revisited
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Optimizing the Latent Space of Generative Networks
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Stereo Magnification: Learning View Synthesis using Multiplane Images
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Neural rerendering in the wild
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NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
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Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich · 2020
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Mvsnerf: Fast generalizable radiance field reconstruction from multi-view stereo
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Neural scene flow fields for space-time view synthesis of dynamic scenes
Zhengqi Li, Simon Niklaus, Noah Snavely, and Oliver Wang · 2021
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NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections
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D-nerf: Neural radiance fields for dynamic scenes
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Non-rigid neural radiance fields: Reconstruction and novel view synthesis of a dynamic scene from monocular video
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Alex Trevithick and Bo Yang · 2021
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Alex Yu, Vickie Ye, Matthew Tancik, and Angjoo Kanazawa · 2021
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Objaverse: A Universe of Annotated 3D Objects
Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli VanderBilt, Ludwig Schmidt, Kiana Ehsani, Aniruddha Kembhavi, and Ali Farhadi · 2022
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Monocular Dynamic View Synthesis: A Reality Check
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Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fields
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CAT3D: Create Anything in 3D with Multi-View Diffusion Models
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