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.

翻译：本文提出Deceptive-NeRF，一种面向少样本NeRF重建的新方法，通过利用扩散模型合成合理的伪观测数据来提升重建质量。该方法包含三个关键步骤：1）从稀疏输入数据重建粗粒度NeRF；2）利用粗粒度NeRF渲染图像并据此生成伪观测数据；3）将输入图像与伪观测数据联合训练精化NeRF模型。我们开发了一种欺骗性扩散模型，能够将粗粒度NeRF的RGB图像与深度图流畅转化为照片级伪观测数据，同时保留场景语义用于重建。此外，我们提出渐进式训练策略，通过当前NeRF渲染结果生成伪观测数据以增强下一迭代的NeRF性能。大量实验表明，即使在极稀疏输入的高度复杂场景中，本方法仍能合成照片级真实感新视角图像。代码将开源。