Neural radiance fields (NeRF) excel at synthesizing new views given multi-view, calibrated images of a static scene. When scenes include distractors, which are not persistent during image capture (moving objects, lighting variations, shadows), artifacts appear as view-dependent effects or 'floaters'. To cope with distractors, we advocate a form of robust estimation for NeRF training, modeling distractors in training data as outliers of an optimization problem. Our method successfully removes outliers from a scene and improves upon our baselines, on synthetic and real-world scenes. Our technique is simple to incorporate in modern NeRF frameworks, with few hyper-parameters. It does not assume a priori knowledge of the types of distractors, and is instead focused on the optimization problem rather than pre-processing or modeling transient objects. More results on our page https://robustnerf.github.io/public.

翻译：神经辐射场（NeRF）在给定静态场景的多视角标定图像时，擅长合成新视角。当场景中存在在图像采集过程中不持久的干扰物（移动物体、光照变化、阴影）时，会出现视差效应或“浮游物”等伪影。为应对干扰物，我们提出一种适用于NeRF训练的稳健估计方法，将训练数据中的干扰物建模为优化问题的异常值。我们的方法能有效从场景中移除异常值，并在合成场景与真实场景中均优于基线方法。该技术易于集成到现代NeRF框架中，仅需少量超参数。它无需预先假设干扰物类型，而是聚焦于优化问题本身，无需预处理或对瞬态物体建模。更多结果请访问我们的页面 https://robustnerf.github.io/public。