论文标题
密度感知的NERF合奏：量化神经辐射场的预测不确定性
Density-aware NeRF Ensembles: Quantifying Predictive Uncertainty in Neural Radiance Fields
论文作者
论文摘要
我们表明，如果考虑密度感知的认知不确定性项，则有效地量化神经辐射场（NERF）中的模型不确定性。在先前的工作中调查的幼稚合奏简单地渲染了RGB图像，以量化因观察到的场景的解释而引起的模型不确定性。相比之下，我们还考虑了各个射线沿单个射线的终止概率，以确定认知模型的不确定性，因为对训练过程中未观察到的场景部分缺乏知识。我们在NERF的既定不确定性量化基准中实现了新的最先进的性能，优于需要对NERF体系结构和培训制度进行复杂更改的方法。我们此外表明，可以将NERF不确定性用于次要视图选择和模型改进。
We show that ensembling effectively quantifies model uncertainty in Neural Radiance Fields (NeRFs) if a density-aware epistemic uncertainty term is considered. The naive ensembles investigated in prior work simply average rendered RGB images to quantify the model uncertainty caused by conflicting explanations of the observed scene. In contrast, we additionally consider the termination probabilities along individual rays to identify epistemic model uncertainty due to a lack of knowledge about the parts of a scene unobserved during training. We achieve new state-of-the-art performance across established uncertainty quantification benchmarks for NeRFs, outperforming methods that require complex changes to the NeRF architecture and training regime. We furthermore demonstrate that NeRF uncertainty can be utilised for next-best view selection and model refinement.
