pixelNeRF: Neural Radiance Fields From One or Few Images
Alex Yu, Vickie Ye, Matthew Tancik, Angjoo Kanazawa
2021Year
627Top-tier citations
Abstract
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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Install the CLIlune papers fulltext 3a10ceab-24e1-41d2-ad8c-a5b971f9b4beCited by top-tier papers627
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Builds on9
- Neural Sparse Voxel FieldsLingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua et al.NeurIPS 2020 · 1,535 citations
- PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human DigitizationShunsuke Saito, Zeng Huang, Ryota Natsume, Shigeo Morishima et al.ICCV 2019 · 1,411 citations
- Soft Rasterizer: A Differentiable Renderer for Image-Based 3D ReasoningShichen Liu, Weikai Chen, Tianye Li, Hao LiICCV 2019 · 789 citations
- Monocular Neural Image Based Rendering With Continuous View ControlJie Song, Xu Chen, Otmar HilligesICCV 2019 · 85 citations
- Neural Point Cloud Rendering via Multi-Plane ProjectionPeng Dai, Yinda Zhang, Zhuwen Li, Shuaicheng Liu et al.CVPR 2020
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