  We present EgoNeRF, a practical solution to reconstruct large-scale
real-world environments for VR assets. Given a few seconds of casually captured
360 video, EgoNeRF can efficiently build neural radiance fields which enable
high-quality rendering from novel viewpoints. Motivated by the recent
acceleration of NeRF using feature grids, we adopt spherical coordinate instead
of conventional Cartesian coordinate. Cartesian feature grid is inefficient to
represent large-scale unbounded scenes because it has a spatially uniform
resolution, regardless of distance from viewers. The spherical parameterization
better aligns with the rays of egocentric images, and yet enables factorization
for performance enhancement. However, the na\"ive spherical grid suffers from
irregularities at two poles, and also cannot represent unbounded scenes. To
avoid singularities near poles, we combine two balanced grids, which results in
a quasi-uniform angular grid. We also partition the radial grid exponentially
and place an environment map at infinity to represent unbounded scenes.
Furthermore, with our resampling technique for grid-based methods, we can
increase the number of valid samples to train NeRF volume. We extensively
evaluate our method in our newly introduced synthetic and real-world egocentric
360 video datasets, and it consistently achieves state-of-the-art performance.
