  This paper proposes a method to reconstruct the neural radiance field with
equirectangular omnidirectional images. Implicit neural scene representation
with a radiance field can reconstruct the 3D shape of a scene continuously
within a limited spatial area. However, training a fully implicit
representation on commercial PC hardware requires a lot of time and computing
resources (15 $\sim$ 20 hours per scene). Therefore, we propose a method to
accelerate this process significantly (20 $\sim$ 40 minutes per scene). Instead
of using a fully implicit representation of rays for radiance field
reconstruction, we adopt feature voxels that contain density and color features
in tensors. Considering omnidirectional equirectangular input and the camera
layout, we use spherical voxelization for representation instead of cubic
representation. Our voxelization method could balance the reconstruction
quality of the inner scene and outer scene. In addition, we adopt the
axis-aligned positional encoding method on the color features to increase the
total image quality. Our method achieves satisfying empirical performance on
synthetic datasets with random camera poses. Moreover, we test our method with
real scenes which contain complex geometries and also achieve state-of-the-art
performance. Our code and complete dataset will be released at the same time as
the paper publication.
