Abstract

Neural radiance fields (NeRF) have demonstrated a promising research direction for novel view synthesis. However, the ex- isting approaches either require per-scene optimization that takes significant computation time or condition on local features which overlook the global context of images. To tackle this shortcoming, we propose the Conditionally Parameterized Neural Radiance Fields (CP-NeRF), a plug-in module that enables NeRF to leverage contextual information from different scales. Instead of optimizing the model parameters of NeRFs directly, we train a Feature Pyramid hyperNetwork (FPN) that extracts view-dependent global and local information from images within or across scenes to produce the model parameters. Our model can be trained end-to-end with standard photometric loss from NeRF. Extensive experiments demonstrate that our method can significantly boost the performance of NeRF, achieving state-of-the-art results in various benchmark datasets.

Proposed Method

Novel View Results

Citation

Acknowledgements

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