CVPR2021

pixelNeRF: Neural Radiance Fields From One or Few Images

Alex Yu, Vickie Ye, Matthew Tancik, Angjoo Kanazawa

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).