  Novel view synthesis via Neural Radiance Fields (NeRFs) or 3D Gaussian
Splatting (3DGS) typically necessitates dense observations with hundreds of
input images to circumvent artifacts. We introduce Deceptive-NeRF/3DGS to
enhance sparse-view reconstruction with only a limited set of input images, by
leveraging a diffusion model pre-trained from multiview datasets. Different
from using diffusion priors to regularize representation optimization, our
method directly uses diffusion-generated images to train NeRF/3DGS as if they
were real input views. Specifically, we propose a deceptive diffusion model
turning noisy images rendered from few-view reconstructions into high-quality
photorealistic pseudo-observations. To resolve consistency among
pseudo-observations and real input views, we develop an uncertainty measure to
guide the diffusion model's generation. Our system progressively incorporates
diffusion-generated pseudo-observations into the training image sets,
ultimately densifying the sparse input observations by 5 to 10 times. Extensive
experiments across diverse and challenging datasets validate that our approach
outperforms existing state-of-the-art methods and is capable of synthesizing
novel views with super-resolution in the few-view setting.
