Neural Radiance Fields (NeRF) have demonstrated effectiveness in synthesizing novel views. However, their reliance on dense inputs and scene-specific optimization has limited their broader applicability. Generalizable NeRFs (Gen-NeRF), while intended to address this, often produce blurring artifacts in unobserved regions with sparse inputs, which are full of uncertainty. In this paper, we aim to diminish the uncertainty in Gen-NeRF for plausible renderings. We assume that NeRF's inability to effectively mitigate this uncertainty stems from its inherent lack of generative capacity. Therefore, we innovatively propose an Indirect Diffusion-guided NeRF framework, termed ID-NeRF, to address this uncertainty from a generative perspective by leveraging a distilled diffusion prior as guidance. Specifically, to avoid model confusion caused by directly regularizing with inconsistent samplings as in previous methods, our approach introduces a strategy to indirectly inject the inherently missing imagination into the learned implicit function through a diffusion-guided latent space. Empirical evaluation across various benchmarks demonstrates the superior performance of our approach in handling uncertainty with sparse inputs.

利用间接扩散引导的 NeRF (ID-NeRF) 框架通过利用精炼的扩散先验指导从生成性的角度解决 Gen-NeRF 中的不确定性，避免了直接约束的混乱取样导致的模型混淆，并经过多个基准测试表明在处理稀疏输入的不确定性方面具有卓越的性能。

通过间接扩散指导驯服稀疏视图通用NeRF中的不确定性