  Virtual tour among sparse 360$^\circ$ images is widely used while hindering
smooth and immersive roaming experiences. The emergence of Neural Radiance
Field (NeRF) has showcased significant progress in synthesizing novel views,
unlocking the potential for immersive scene exploration. Nevertheless, previous
NeRF works primarily focused on object-centric scenarios, resulting in
noticeable performance degradation when applied to outward-facing and
large-scale scenes due to limitations in scene parameterization. To achieve
seamless and real-time indoor roaming, we propose a novel approach using
geometry-aware radiance fields with adaptively assigned local radiance fields.
Initially, we employ multiple 360$^\circ$ images of an indoor scene to
progressively reconstruct explicit geometry in the form of a probabilistic
occupancy map, derived from a global omnidirectional radiance field.
Subsequently, we assign local radiance fields through an adaptive
divide-and-conquer strategy based on the recovered geometry. By incorporating
geometry-aware sampling and decomposition of the global radiance field, our
system effectively utilizes positional encoding and compact neural networks to
enhance rendering quality and speed. Additionally, the extracted floorplan of
the scene aids in providing visual guidance, contributing to a realistic
roaming experience. To demonstrate the effectiveness of our system, we curated
a diverse dataset of 360$^\circ$ images encompassing various real-life scenes,
on which we conducted extensive experiments. Quantitative and qualitative
comparisons against baseline approaches illustrated the superior performance of
our system in large-scale indoor scene roaming.
