  We present a method to synthesize novel views from a single $360^\circ$
panorama image based on the neural radiance field (NeRF). Prior studies in a
similar setting rely on the neighborhood interpolation capability of
multi-layer perceptions to complete missing regions caused by occlusion, which
leads to artifacts in their predictions. We propose 360FusionNeRF, a
semi-supervised learning framework where we introduce geometric supervision and
semantic consistency to guide the progressive training process. Firstly, the
input image is re-projected to $360^\circ$ images, and auxiliary depth maps are
extracted at other camera positions. The depth supervision, in addition to the
NeRF color guidance, improves the geometry of the synthesized views.
Additionally, we introduce a semantic consistency loss that encourages
realistic renderings of novel views. We extract these semantic features using a
pre-trained visual encoder such as CLIP, a Vision Transformer trained on
hundreds of millions of diverse 2D photographs mined from the web with natural
language supervision. Experiments indicate that our proposed method can produce
plausible completions of unobserved regions while preserving the features of
the scene. When trained across various scenes, 360FusionNeRF consistently
achieves the state-of-the-art performance when transferring to synthetic
Structured3D dataset (PSNR~5%, SSIM~3% LPIPS~13%), real-world Matterport3D
dataset (PSNR~3%, SSIM~3% LPIPS~9%) and Replica360 dataset (PSNR~8%, SSIM~2%
LPIPS~18%).
