  We study to generate novel views of indoor scenes given sparse input views.
The challenge is to achieve both photorealism and view consistency. We present
SparseGNV: a learning framework that incorporates 3D structures and image
generative models to generate novel views with three modules. The first module
builds a neural point cloud as underlying geometry, providing contextual
information and guidance for the target novel view. The second module utilizes
a transformer-based network to map the scene context and the guidance into a
shared latent space and autoregressively decodes the target view in the form of
discrete image tokens. The third module reconstructs the tokens into the image
of the target view. SparseGNV is trained across a large indoor scene dataset to
learn generalizable priors. Once trained, it can efficiently generate novel
views of an unseen indoor scene in a feed-forward manner. We evaluate SparseGNV
on both real-world and synthetic indoor scenes and demonstrate that it
outperforms state-of-the-art methods based on either neural radiance fields or
conditional image generation.
