pixelNeRF: Neural Radiance Fields From One or Few Images
Alex Yu, Vickie Ye, Matthew Tancik, Angjoo Kanazawa
2021年份
627顶会引用
摘要
We present pixelNeRF, a learning framework that predicts a Neural Radiance Field (NeRF) representation from a single (top) or few posed images (bottom). PixelNeRF can be trained on a set of multi-view images, allowing it to generate plausible novel view synthesis from very few input images without test-time optimization (bottom left). In contrast, NeRF has no generalization capabilities and performs poorly when only three input views are available (bottom right).
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引用它的顶会 Paper627
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它引用的顶会 Paper9
- Neural Sparse Voxel FieldsLingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua 等NeurIPS 2020 · 被引用 1,535 次
- PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human DigitizationShunsuke Saito, Zeng Huang, Ryota Natsume, Shigeo Morishima 等ICCV 2019 · 被引用 1,411 次
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- Monocular Neural Image Based Rendering With Continuous View ControlJie Song, Xu Chen, Otmar HilligesICCV 2019 · 被引用 85 次
- Neural Point Cloud Rendering via Multi-Plane ProjectionPeng Dai, Yinda Zhang, Zhuwen Li, Shuaicheng Liu 等CVPR 2020
相关 Paper
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- MonoNeRF: Learning Generalizable NeRFs from Monocular Videos without Camera PosesYang Fu, Ishan Misra, Xiaolong WangICML 2023 · 被引用 13 次
