用空域先验快速清除NeRF的浮点伪影，提升未见区域画质。
Optimize the Unseen -- Fast NeRF Cleanup with Free Space Prior
- 基于全局空域先验的后处理方法，通过最大后验估计优化模型参数。
- 在30秒内完成清理训练，推理速度比现有方法快2.5倍。
- 无需额外显存，适合对画质敏感且追求效率的3D重建应用。
神经辐射场（NeRF）虽能实现逼真的新视角合成，但依赖光度重建常引入称为"浮点伪影"的瑕疵，尤其在训练相机未覆盖区域更为明显。本文提出一种快速、后处理式的NeRF清理方法，通过施加空域先验，有效消除这些伪影，同时不破坏已有观测区域的表示。不同于依赖最大似然估计或复杂局部数据驱动先验的方法，本方法采用最大后验（MAP）框架，在假设未见区域应为空的简单全局先验下，选择最优模型参数。该方法可同时清理可见与不可见区域的伪影，显著提升困难场景下的新视角质量。相比现有清理模型，本方法推理速度提升2.5倍，仅需原始NeRF内存，且清理训练耗时低于30秒。代码将公开。
原文摘要 · Abstract (English)
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.
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