pixelNeRF: Neural Radiance Fields from One or Few Images

Understand

We propose pixelNeRF, a learning framework that predicts a continuous neural scene representation conditioned on one or few input images.

• The existing approach for constructing neural radiance fields involves optimizing the representation to every scene independently, requiring many calibrated views and significant compute time.
• We take a step towards resolving these shortcomings by introducing an architecture that conditions a NeRF on image inputs in a fully convolutional manner.
• This allows the network to be trained across multiple scenes to learn a scene prior, enabling it to perform novel view synthesis in a feed-forward manner from a sparse set of views (as few as one).