We introduce Deceptive-NeRF, a novel methodology for few-shot NeRF reconstruction, which leverages diffusion models to synthesize plausible pseudo-observations to improve the reconstruction. This approach unfolds through three key steps: 1) reconstructing a coarse NeRF from sparse input data; 2) utilizing the coarse NeRF to render images and subsequently generating pseudo-observations based on them; 3) training a refined NeRF model utilizing input images augmented with pseudo-observations. We develop a deceptive diffusion model that adeptly transitions RGB images and depth maps from coarse NeRFs into photo-realistic pseudo-observations, all while preserving scene semantics for reconstruction. Furthermore, we propose a progressive strategy for training the Deceptive-NeRF, using the current NeRF renderings to create pseudo-observations that enhance the next iteration's NeRF. Extensive experiments demonstrate that our approach is capable of synthesizing photo-realistic novel views, even for highly complex scenes with very sparse inputs. Codes will be released.

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