  Recent implicit neural representations have shown great results for novel
view synthesis. However, existing methods require expensive per-scene
optimization from many views hence limiting their application to real-world
unbounded urban settings where the objects of interest or backgrounds are
observed from very few views. To mitigate this challenge, we introduce a new
approach called NeO 360, Neural fields for sparse view synthesis of outdoor
scenes. NeO 360 is a generalizable method that reconstructs 360{\deg} scenes
from a single or a few posed RGB images. The essence of our approach is in
capturing the distribution of complex real-world outdoor 3D scenes and using a
hybrid image-conditional triplanar representation that can be queried from any
world point. Our representation combines the best of both voxel-based and
bird's-eye-view (BEV) representations and is more effective and expressive than
each. NeO 360's representation allows us to learn from a large collection of
unbounded 3D scenes while offering generalizability to new views and novel
scenes from as few as a single image during inference. We demonstrate our
approach on the proposed challenging 360{\deg} unbounded dataset, called NeRDS
360, and show that NeO 360 outperforms state-of-the-art generalizable methods
for novel view synthesis while also offering editing and composition
capabilities. Project page:
https://zubair-irshad.github.io/projects/neo360.html
