This paper introduces Deceptive-NeRF, a new method for enhancing the quality of reconstructed NeRF models using synthetically generated pseudo-observations, capable of handling sparse input and removing floater artifacts. Our proposed method involves three key steps: 1) reconstruct a coarse NeRF model from sparse inputs; 2) generate pseudo-observations based on the coarse model; 3) refine the NeRF model using pseudo-observations to produce a high-quality reconstruction. To generate photo-realistic pseudo-observations that faithfully preserve the identity of the reconstructed scene while remaining consistent with the sparse inputs, we develop a rectification latent diffusion model that generates images conditional on a coarse RGB image and depth map, which are derived from the coarse NeRF and latent text embedding from input images. Extensive experiments show that our method is effective and can generate perceptually high-quality NeRF even with very sparse inputs.

翻译：本文提出Deceptive-NeRF，一种利用合成生成的伪观测来提升重建NeRF模型质量的新方法，能够处理稀疏输入并消除浮游伪影。我们提出的方法包含三个关键步骤：1) 从稀疏输入重建粗粒度NeRF模型；2) 基于粗粒度模型生成伪观测；3) 使用伪观测精炼NeRF模型以产生高质量重建。为生成能够忠实保持重建场景身份特征且与稀疏输入一致的逼真伪观测，我们开发了一种校正潜在扩散模型，该模型以粗RGB图像与深度图（源自粗粒度NeRF）以及输入图像的潜在文本嵌入为条件生成图像。大量实验表明，该方法行之有效，即便在输入极为稀疏的情况下，也能生成感知质量高的NeRF。