  Cross-view self-localization is a challenging scenario of visual place
recognition in which database images are provided from sparse viewpoints.
Recently, an approach for synthesizing database images from unseen viewpoints
using NeRF (Neural Radiance Fields) technology has emerged with impressive
performance. However, synthesized images provided by these techniques are often
of lower quality than the original images, and furthermore they significantly
increase the storage cost of the database. In this study, we explore a new
hybrid scene model that combines the advantages of view-invariant appearance
features computed from raw images and view-dependent spatial-semantic features
computed from synthesized images. These two types of features are then fused
into scene graphs, and compressively learned and recognized by a graph neural
network. The effectiveness of the proposed method was verified using a novel
cross-view self-localization dataset with many unseen views generated using a
photorealistic Habitat simulator.
