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In recent years, the performance of novel view synthesis using perspectiveimages has dramatically improved with the advent of neural radiance fields(NeRF). This study proposes two novel techniques that effectively build NeRFfor 360{\textdegree} omnidirectional images. Due to the characteristics of a360{\textdegree} image of ERP format that has spatial distortion in their highlatitude regions and a 360{\textdegree} wide viewing angle, NeRF's general raysampling strategy is ineffective. Hence, the view synthesis accuracy of NeRF islimited and learning is not efficient. We propose two non-uniform ray samplingschemes for NeRF to suit 360{\textdegree} images - distortion-aware raysampling and content-aware ray sampling. We created an evaluation datasetSynth360 using Replica and SceneCity models of indoor and outdoor scenes,respectively. In experiments, we show that our proposal successfully builds360{\textdegree} image NeRF in terms of both accuracy and efficiency. Theproposal is widely applicable to advanced variants of NeRF. DietNeRF, AugNeRF,and NeRF++ combined with the proposed techniques further improve theperformance. Moreover, we show that our proposed method enhances the quality ofreal-world scenes in 360{\textdegree} images. Synth360:https://drive.google.com/drive/folders/1suL9B7DO2no21ggiIHkH3JF3OecasQLb.

Non-uniform Sampling Strategies for NeRF on 360{\textdegree} images