  In recent years, the performance of novel view synthesis using perspective
images has dramatically improved with the advent of neural radiance fields
(NeRF). This study proposes two novel techniques that effectively build NeRF
for 360{\textdegree} omnidirectional images. Due to the characteristics of a
360{\textdegree} image of ERP format that has spatial distortion in their high
latitude regions and a 360{\textdegree} wide viewing angle, NeRF's general ray
sampling strategy is ineffective. Hence, the view synthesis accuracy of NeRF is
limited and learning is not efficient. We propose two non-uniform ray sampling
schemes for NeRF to suit 360{\textdegree} images - distortion-aware ray
sampling and content-aware ray sampling. We created an evaluation dataset
Synth360 using Replica and SceneCity models of indoor and outdoor scenes,
respectively. In experiments, we show that our proposal successfully builds
360{\textdegree} image NeRF in terms of both accuracy and efficiency. The
proposal is widely applicable to advanced variants of NeRF. DietNeRF, AugNeRF,
and NeRF++ combined with the proposed techniques further improve the
performance. Moreover, we show that our proposed method enhances the quality of
real-world scenes in 360{\textdegree} images. Synth360:
https://drive.google.com/drive/folders/1suL9B7DO2no21ggiIHkH3JF3OecasQLb.
