Neural Radiance Fields (NeRF) have advanced photorealistic novel view synthesis, but their reliance on photometric reconstruction introduces artifacts, commonly known as "floaters". These artifacts degrade novel view quality, especially in areas unseen by the training cameras. We present a fast, post-hoc NeRF cleanup method that eliminates such artifacts by enforcing our Free Space Prior, effectively minimizing floaters without disrupting the NeRF's representation of observed regions. Unlike existing approaches that rely on either Maximum Likelihood (ML) estimation to fit the data or a complex, local data-driven prior, our method adopts a Maximum-a-Posteriori (MAP) approach, selecting the optimal model parameters under a simple global prior assumption that unseen regions should remain empty. This enables our method to clean artifacts in both seen and unseen areas, enhancing novel view quality even in challenging scene regions. Our method is comparable with existing NeRF cleanup models while being 2.5x faster in inference time, requires no additional memory beyond the original NeRF, and achieves cleanup training in less than 30 seconds. Our code will be made publically available.

翻译：神经辐射场（NeRF）在逼真的新视角合成方面取得了进展，但其对光度重建的依赖引入了伪影，通常称为“漂浮物”。这些伪影会降低新视角的质量，尤其是在训练相机未观测到的区域。我们提出了一种快速的后处理NeRF清理方法，通过强制执行我们的自由空间先验来消除此类伪影，有效减少漂浮物而不破坏NeRF对已观测区域的表示。与现有方法依赖于最大似然（ML）估计来拟合数据或使用复杂、局部数据驱动先验不同，我们的方法采用最大后验（MAP）方法，在一个简单的全局先验假设下选择最优模型参数，即未观测区域应保持为空。这使得我们的方法能够清理已观测和未观测区域的伪影，即使在具有挑战性的场景区域也能提升新视角质量。我们的方法与现有NeRF清理模型性能相当，同时推理速度快2.5倍，除原始NeRF外无需额外内存，且清理训练在30秒内完成。我们的代码将公开提供。