  Neural Radiance Fields (NeRF) has emerged as the state-of-the-art method for
novel view generation of complex scenes, but is very slow during inference.
Recently, there have been multiple works on speeding up NeRF inference, but the
state of the art methods for real-time NeRF inference rely on caching the
neural network output, which occupies several giga-bytes of disk space that
limits their real-world applicability. As caching the neural network of
original NeRF network is not feasible, Garbin et al. proposed "FastNeRF" which
factorizes the problem into 2 sub-networks - one which depends only on the 3D
coordinate of a sample point and one which depends only on the 2D camera
viewing direction. Although this factorization enables them to reduce the cache
size and perform inference at over 200 frames per second, the memory overhead
is still substantial. In this work, we propose SqueezeNeRF, which is more than
60 times memory-efficient than the sparse cache of FastNeRF and is still able
to render at more than 190 frames per second on a high spec GPU during
inference.
