Improving NeRF with Height Data for Utilization of GIS Data
Hinata Aoki Takao Yamanaka
Neural Radiance Fields (NeRF) has been applied to various tasks related to
representations of 3D scenes. Most studies based on NeRF have focused on a
small object, while a few studies have tried to reconstruct large-scale scenes
although these methods tend to require large computational cost. For the
application of NeRF to large-scale scenes, a method based on NeRF is proposed
in this paper to effectively use height data which can be obtained from GIS
(Geographic Information System). For this purpose, the scene space was divided
into multiple objects and a background using the height data to represent them
with separate neural networks. In addition, an adaptive sampling method is also
proposed by using the height data. As a result, the accuracy of image rendering
was improved with faster training speed.